In colloquial use, a problem is something to be overcome. When people say they have money problems, the problem may be overcome simply by having more money. When someone has a mathematical problem, the problem may be overcome by mathematics. The thing or process used to overcome a problem is called a solution.
At this point, it may seem a little strange for me to define what is a common word. Ah, but precision and clarity of mind are required in order to solve problems with ML. You have to be precise about what exactly you are trying to solve.
Problems can be broken down into subproblems. But at some point, it no longer makes sense to break down those problems any further. I put it to the reader that there are different types of problems out there. So numerous are the types of problems that it is not worthwhile enumerating them all. Nonetheless, the urgency of a problem should be considered.
If you're building a photo organization tool (perhaps you are planning to rival Google Photos or Facebook), then recognizing faces in a photo is less urgent than knowing where to store, and how to retrieve a photo. If you do not know how to solve the latter, all the knowledge in the world to solve the former would be wasted.
I argue that urgency, despite its subjectivity, is a good metric to use when considering subproblems of the larger problem. To use a set of more concrete examples, consider three scenarios that all require some sort of ML solutions, but the solutions required are of different urgency. These examples are clearly made up examples, and have little or no bearing on real life. Their entire purpose is to make a point.
First, consider a real estate intelligence business. The entire survival of the business depends on being able to correctly predict the prices of houses to be sold, although perhaps they also make their money on some form of second market. To them, the ML problem faced is urgent. They would have to fully understand the ins and outs of the solution, otherwise they risk going out of business. In the view of the popular urgency/importance split, the ML problem can also be considered important and urgent.
Second, consider an online supermarket. They would like to know which groupings of products sell best together so they can bundle them to appear more competitive. This is not the core business activity, hence the ML problem faced is less urgent than the previous example. Some knowledge about how the solution works would be necessary. Imagine their algorithm says that they should bundle diarrhea medication with their home brand food products. They'd need to be able to understand how the solution came to that.
Lastly, consider the aforementioned photo application. Facial recognition is a nice bonus feature, but not the main feature. Therefore, the ML problem is least urgent amongst the three.
Different urgencies lead to different requirements when it comes to solving the problems.
