There are two other ways to contrast classical and machine learning systems. One is deterministic and the other is probabilistic.
Let’s dig in and see what these two words mean.
For a deterministic system, there must be an enormous, predetermined structure of routes—a gigantic database of possibilities from which the machine can make its choice. If a certain route leads to the destination, then the machine flags it as “YES”. If not, it flags it as “NO”. This is basically binary thinking: on or off, yes or no. This is the essence of a computer program. The answer is either true or false, not a confidence value.
Machine learning is probabilistic. It never says “YES” or “NO”. Machine learning is analog (like waves gradually going up and down) rather than binary (like arrows pointing upward and downward). Machine learning constructs every possible route to a destination and compares them in real time, including all the variables such as changing traffic. So, a machine learning system doesn’t say, “This is the fastest route.” It says something like, “I am 84% confident that this route will get you there in the shortest time.” You might have seen this yourself if you’ve traveled in a car with an up-to-date GPS navigation system that offers you two or three choices with estimated times.
If machine learning offers only probabilities, who makes the final decision?
This can literally be a life-and-death question. Suppose you have a serious disease and your doctor offers you a choice. Do you want your doctor to prescribe your treatment, or do you want the treatment that a machine learning system determines is most likely to succeed?