If 1 in 10,000 people have a disease, then a "test" which always reports the patient doesn't have the disease will be correct 99.99% of the time. "99.99% accuracy" should be "looked at with skepticism" in that it doesn't tell you what you need to know to understand the quality of a a test for a rare disease (a classifier under conditions of severe class imbalance); at a minimum, you would want to understand it's false positive and false negative rate, not (just) it's overall error rate.
You appear not to have understood probability theory my friend. You will never get 100% in this universe for anything. What if "its a simulation" or "a dream" arguments ensures you never acheive 100%.
Bayes probability theory will be a good start for you.
Well, I do agree that all measurements contain error, but the point wasn't that the error rate would be greater than 0% but that a single headline summary of error can't always distinguish between good and bad tests.
See example "A": https://en.wikipedia.org/wiki/Base_rate_fallacy