I have to point out that machine learning is not just about making predictions from data. Machine learning can also be about determining something without being explicitly told to do so. For example, ML can be used to classify data and learn how to improve that classification over time every time it classifies something.
I know that may sound like semantics to some, but there is a difference between manipulation versus definition just as there is a difference between supervised and unsupervised techniques among other things. While one approach may not be good for one problem, other approaches may be better.
For example, every time you go out to run, learning what is you are doing when you do run in order to help you run better or maybe make that experience more enjoyable. For example, analyzing your exact location via GPS coordinates, analyzing data that may be relevant to that location and making you aware of certain events or locations that you may want to know. Like a delay, your favorite store, a unpredicted change in the weather, and so forth. Then maybe classifying these events based on how you react to them in order to learn from what is relevant and not relevant to you and how you are reacting to them over time. That way it's not just spamming you all the time or forcing you to define a series of preferences.
In the Google world, they may also dip into other sources of data you use for other problems and tasks in order to inform itself. For example, if you are looking at a menu online for a restaurant, it may use that information to alert you that the restaurant is on the same heading you are running. Or if you looked up a certain movie on IMDB that you watched last night, let you know a certain actor of that movie you watched is doing an event nearby at that time you are jogging.