I found it amusing that one of the poll answers proposed ML as an alternative to traditional programming, which was not the expected answer. In that sense, I do agree the correct answer was the most agreeable one.
That said, some of the next poll questions I thought demonstrated the point of the first, which is to say there are many, many ways to use either to solve a problem. In some of those cases, such as writing a procedure, there's a clearly better choice and a clearly more cumbersome one.
I'd put it more like this. Fundamentally, traditional programming languages "decide" what to do based on some collection of rules. They express a primitive aspect of a traditional computer, the logic gate: something is true or false. Complexity in decision-making builds up from there.
Machine learning is intended as a replacement to traditional programming, I argue, but in a particular case. To wit: when the amount of data available is Big, and the values of its features show some propensity for changing (or evolving) over time, machine learning is the more likely choice.
Traditional programming makes decisions through a pathway of rules. Machine learning operates on assigning meaning to data (e.g., labelling, feature reduction), or associating data (e.g., classification), provided there is enough data to determine statistical significance, and then accuracy of its inferences.
I argue you can replace traditional programming with machine learning for the same reasons, in hindsight, we've been using traditional programming in lieu of machine learning. The only reason we didn't, any sooner than we did, was for lack of resources to do it. Now that we have enough to do both, we can freely our waste our time committing to the wrong approach, as we often do, or selecting the more plausible approach.
If you have any experience watching customers choose ML for a project because ML was the hot new thing, I imagine you can see my point of view.
