There certainly are tasks where a 2% failure rate is fine, but even more importantly, where we see ML/DL having the biggest impact today is in regards to comple

There certainly are tasks where a 2% failure rate is fine, but even more importantly, where we see ML/DL having the biggest impact today is in regards to complex system-type problems, where there is often a less-than-discrete notion of failure. Looking at apps we use every day, almost all of them owe some core feature to ML/DL. ETA prediction, translation, search, spam filtering, speech synthesis, autocomplete, recommendation engines, fraud detection—and that's not even touching the world of computer vision behind nearly every popular photo app. A key understanding gap in the general public's knowledge of ML is that people think AI === Skynet, and they've therefore been lied to about the field's progress and impact, when in reality, they probably interface with a dozen pieces of technology that are built on top of recent breakthroughs in ML/DL.