You don't need AI to find values for free parameters. Unless you consider all of statistics to be a subfield of AI.
Machine Learning = statistics + linear algebra + computer science, mostly.
Naive Bayes and Graphical Models are pure statistics, but they are mostly used for toy problems. Machine Learning scales these approaches to high dimensionality problems, and tasks where data is abundant.
I think they just feel "toy" because they've been used with great accuracy for so long.
Complex problems are rarely solved by a single approach. The harder the problem the more likely a suite will be used, and often naive bayes will be part of that in some capacity.
Naive Bayes is a common baseline solution because it's very simple to implement and very fast to run; however, pretty much any other ML method gives better accuracy than Naive Bayes. Sometimes the accuracy improvement is small and doesn't justify the highly increased computing cost, though, so Naive Bayes becomes the appropriate method to use, but it's not because of its accuracy.
Machine learning is the use of computers to make decisions (or classifications) based on data without human intervention.
Just because Bayesian isn’t over-hyped doesn’t mean it doesn’t solve real problems. Not everything can be solved with ML, unless the problem is getting more investor money.
Having worked with Oracle's version, and seen entire systems buult out of that, I dread it.
Now, the biggest difference that I can see is that, within machine learning contexts, people are more concerned with the quality of the predictions than the interpretation of the independent variables. That’s not hard and fast, but it seems to be a common thread.
And for some problems, making a good prediction is really the right thing. In other cases, understanding the mechanisms you might use to effect an outcome is more important. Both are valid uses of statistical methods, depending on the problem.
I think within the realm of classical statistics, Bayesian methods’ super power is in being able to generate results that are much easier to communicate to lay people. Also Bayesian methods are nice if you want to do sensitivity analysis in a principled up front kind of way.
But I could imagine using those methods in an ML context even if they aren’t the current darlings of the methodological pantheon.
AI is about substituting human decision makers or other conventional computer decision algorithms. AI is used for situational decision making. It needs to be quick (like the conventional algorithms) while also recognizing the nuance and multi-dimemaional nature of a lot of decisions. Robustness and quickness are often valued more than precision.
IOW the GP is only using ML for everything because it is a buzzword.
What you use as description for AI tempts me to use the "use it as a buzzword" angle back at you here - it is a stereotypical description of some use cases of AI approaches; but there are others - [machine] learning, knowledge representation, planning and scheduling, reasoning (both formal logic reasoning and also reasoning under uncertainty e.g. Bayesian approaches), intelligent agent representation, etc, are all parts of the AI field.
Also, the argument that it's pure statistics doesn't really fly - support vector machines, random forests or deep learning multilayer perceptrons are just as much statistics as probabilistic graphical models.
Bayesian models are also advantageous in that they are much easier to describe to stakeholders. Neural networks, on the other hand, are highly difficult(if not impossible in many cases) to describe how they function.