An obvious but often neglected fact is the overemphasized value of accuracy as a performance metric. In a two-class problem where 99% of the cases are of 0 (Not a spam email), achieving an accuracy of 99% is as easy as classifying all emails as safe. Sensitivity, specificity, and other metrics exist for a reason.
The story of Waymo, Google's self-driving car, resembles the value of solving the remaining 1% of the problem where conventional machine learning gets stuck due to the limitations of training data. If 1% of the error turns into a make or break point, one needs to get creative. On a long tail that extends to infinity, walking faster or running does not probably help as much as a leap of imagination.
I must note that it's not fair to expect an autonomous car to be "error-free" given we do not expect human drivers to perform error-free at the driver license exams and road tests. The two will just make different errors.
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