I don't doubt it and I don't doubt that M.I.T. will create an intensive AI college. My point was that a lot of universities, even distinguished ones, are recognizing that there's a real demand and hype for "AI/Data Science" degrees and in an effort to maximize enrollment and appeal they often minimize the mathematical and statistical requirements.
I don't believe that you need an advanced degree to become a component ML engineer, but the math/stats is necessary pre-requisite and these pre-reqs are often poorly defined. At my college, the only pre-req to the graduate-level ML course was the freshman level intro to stats class and multivariable calculus. About 50% of the class dropped when they realized they didn't know how to construct Gaussian models or perform convex optimization.
Maybe it's just me, but having gone through all the stats and maths behind ML, it seems like ultimately the less interesting part (though to be fair, algorithm design is similarly uninteresting for similar reasons). We're talking about a lot of very long-in-the-tooth concepts that are still the basis of many, many approaches. They're important, but it's well-worn territory.
The underappreciated parts of AI, in my experience, are more philosophical; about the nature of reasoning and approximating or beating human thought. About autonomous agents, non zero-sum games and ethical, non-maximizing functions. There's a huge overlap with logic (philosophical and mathematical) here, and I haven't seen that really broached at any of these big programs.
It definitely is not just you. I spent my first two years of PhD wrapping my head around the stats and maths commonly used in ML, and realized that mathematically (as "theoretical" ML is practiced today), most answers are already provided in classical work of statisticians and probabilists. There are many fascinating questions of probability theory and statistics, but most have little to do with AI. In fact, in terms of the biggest empirical success story (deep neural networks), there are essentially no theorems providing a solid conceptual leap of understanding. Mikhail Gromov goes one step further regarding the lack of theory for neural networks (https://www.youtube.com/watch?v=g4Wl3Ggho6k), and provides a fascinating overview of his thoughts in:
https://www.ihes.fr/~gromov/category/ergosystems/
I am interested in the points you raise, but also realized that I would not find a good environment for it at MIT in EECS, for reasons that are rather obvious from the article's subtext. As such, the last year or so has been spent in a search for good alternatives in terms of research, and I am slowly finding answers. I am happy to discuss more over email.
Long story short: you are certainly not the only one who thinks that way.
EDIT: added a video link to Mikhail Gromov's actual views for better accuracy.
I see a lot of graduate students focusing on practical uses of ML algorithms as a result of this. A lot of people don't realize that a good portion of the math is already figured out, and that it's in the implementation of these algorithms that they can find more interesting results.
It must be noted, though, that "approximating human thought" is just one direction of investigation - and not the most important one at that; as interesting as it may be, it makes almost as much sense as trying to have computers resemble human brains. In other words, the true AI, when it arrives, will not think like us humans (even if at some level it might pretend that it does).
> ethical
The AI will be just as "ethical" as a computer or an assault rifle.
Sort of my point. Current (by that I mean post-early 20th century) approaches were to mimic what we believe to be human reasoning. That's clearly limited.
> The AI will be just as "ethical" as a computer or an assault rifle.
I think that's reductive. Reasoning is not entirely analytical. There are other implications and concerns to artificial sentience.
You can have linear algebra and stats for the ML engineer classes as coursework. Most sciences have an applied linear algebra class which doesn't require the proof heavy math version. And most colleges require a year of stats. I think though while linear algebra for the sake of linear algebra is good, it isn't strictly necessary for AI and you can learn what you need. If you want to go deeper then go deeper. You could argue you need a class in ODEs and stochastic optimal control theory to understand RL, but you don't really. Maybe in grad school, maybe in some research area inside of RL but not in undergrad. Of course the linear algebra will help you. The best thing would be that the RL, control systems, stats, physicists and other related folks would start speaking the same language.
I don't believe that you need an advanced degree to become a component ML engineer, but the math/stats is necessary pre-requisite and these pre-reqs are often poorly defined. At my college, the only pre-req to the graduate-level ML course was the freshman level intro to stats class and multivariable calculus. About 50% of the class dropped when they realized they didn't know how to construct Gaussian models or perform convex optimization.