[mrxd]

ML is useful for many things. I'm asking the question of whether prediction is useful, and whether it is accurate to describe ML as making predictions.

The reason to raise those questions is that for many people, the word prediction has connotations of surveillance and control, so it is best not to use it loosely.

The meaning of the word "predict" is to indicate a future event, so it doesn't make grammatical sense to put a present tense verb after it, as you have done in "Predict what features a user is most interested in." Aside from the verb being in the present tense, being interested in something is not an event.

You can't predict a present state of affairs. If I look out the window and see that it is raining, no one would say that I've predicted the weather. If I come to that conclusion indirectly (e.g. a wet umbrella by the door), that would not be considered a prediction either because it's in the present. The accurate term for this is "inference", not "prediction".

The usage of the word predict is also incorrect from the point of view of an A/B test. If your ML model has truly predicted that your users will purchase a particular product, they will purchase it regardless of which condition they are in. But this is the null hypothesis, and the ML model is being introduced in the treatment group to disprove this.

[soared]

You can predict a present state of affairs if they are unknown to you.

I predict the weather in NYC is 100F. I don’t know whether or not that is true.

Really a pedantic argument, but to appease your phrasing you can reword my comment with “We predict an increase in conversion rate if we assume the user is interested in feature x more than feature y”

[mrxd]

That is a normal usage in the tech industry, but that's not how ordinary people use that word. More importantly, it's not how journalists use that word.

In ordinary language, you are making inferences about what users are interested in, then making inferences about what products are relevant to that interest. The prediction is that putting relevant products in front of users will make them buy more - but that is a trivial prediction.