I really do not like this move. AI and Machine Learning require graduate-level mathematical and computational skills. I don't think it's productive to pretend that we can train someone to be even remotely useful in these fields in four years of an undergraduate education. It sounds like an attempt to get around the fundamentals of csci to "skip to the interesting bits," which will produce graduates with a cursory kno…
Yeah, not really. A lot of day-to-day work in ML requires rudimentary math, at most. I know PhDs who quickly get discouraged with ML because they're suddenly spending 95% of their time doing the grunt work. It would be a boon if we could hire non-PhDs who are competent in the fundamentals of signals, algorithms, statistics, and experiment design.
If you're aspiring to work in ML, what major do you choose now? Statistics? EE? CS? Math?
None of these are ideal. If you're doing CS, you're probably too busy with compilers or DB courses to get a proper education in signal processing, information theory, stochastic processes, etc. If you're in EE, you're too busy soldering circuits and to learn about data structures, algorithms, or software engineering courses. There's a lot of room for improvement here.
Even at the graduate level, most of our EE interns and new hires can't solve FizzBuzz, while the CS majors can't properly design a scientific experiment to save their lives.