Progress Slows After Machines Surpass Human-Level Performance

When does comparing to human performance still help an ML system that already surpasses average human-level accuracy on the dev/test set?

Even after a system surpasses average human-level performance on the full dev/test set, human comparison can still provide value on specific data subsets.

On subsets where humans outperform the algorithm, humans can still provide better _____, useful intuition, and a desired performance target.

Match each benefit of using human comparison on a human-better data subset to its description.

Order the reasoning steps for deciding whether human-comparison techniques still apply after your system surpasses average human-level performance.

In the MLY speech recognition example, at which task does the system surpass humans while humans still outperform the system at a different task?

Once a system's average performance on the full dev set exceeds human-level performance, human-comparison techniques like error analysis and human labeling no longer apply at all.

According to MLY, human-comparison techniques apply 'so long as there are dev set examples where humans are _____ and your algorithm is wrong.'

Match each element of the MLY speech recognition example to its role in the human-better-subset framework.

Order the steps for leveraging a human-better subset—like rapidly spoken speech in the MLY example—to improve an ML system.