Looking at the course list (https://www.cs.cmu.edu/bs-in-artificial-intelligence/curricu...), I'd struggle to believe that students are going to come out of this strong enough to be effective in AI. I never went to CMU, so I don't know how rigorous the "Modern Regression" course is for actually getting people sufficiently well-grounded in statistics to be able to overcome p-hacking and similar fallacies in analysis. I also would much like to see some sort of capstone project showing that the student can actually pull the AI together to make something complete, rather than having a merely theoretically background.
I took Modern Regression at CMU for my Statistics minor: yes, it's rigorous, with an emphasis on linear regression (and the necessity for proper p-value handling), with plenty of matrix algebra and statistical theory.
How is that different from any other Undergraduate program. At the end of the day undergraduate degrees are like the bare essentials of education - there is a life long journey of learning in any technical field.
The short of it is that AI isn't an undergraduate-level specialization. Having a demonstrated capstone project, a full system that someone could point to when in an interview, would go a long way to ameliorating concerns. Masters degrees generally have a thesis that qualifies, and it wouldn't be hard to make a senior project be a requirement for an undergraduate degree (my CS department had such a requirement).
I am admittedly somewhat biased, since I have a Bachelor's in CS from CMU, but I'm not really seeing anything problematic here. (Not to mention, many CMU students will take on minors as well, though I'm totally blanking on whether a minor is required.)
Modern Regression is a 400-level statistics course at a school that values statistics and AI.
Many of the higher-level electives will involve sophisticated projects--not 2 semester capstones to be sure, but month-or-longer open ended projects.
Not really sure what you're expecting out of undergrad programs, to be honest.
