Why does progress usually slow down after an algorithm surpasses human-level performance?

True or False: Progress is typically faster while machines are still catching up to human-level performance.

Once humans have a hard time identifying examples the algorithm is clearly getting wrong, only a _____ of human-comparison techniques still apply.

Match each performance situation to its typical progress rate.

Order the reasoning that explains why progress slows after surpassing human-level performance.

Analyze why surpassing human-level performance changes the pace of ML progress.

Diagnose why a loan-approval algorithm's improvement has stalled.

Name two applications where the source says machines already surpass human-level performance.

Which condition allows more human-comparison techniques to remain applicable during ML development?

True or False: All human-comparison techniques remain equally useful after an algorithm surpasses human-level performance.