I don't necessarily see the problem as tenure, but that "computer science" has always been an ill-defined discipline with strange bedfellows. In my department the professors work on such different areas that they can't even communicate, which makes agreeing on a common curriculum difficult. The theory guys say "oh, of course theory is fundamental to computer science, everyone needs to take it." And then the systems profs say the same thing about networking, databases, and OS. Then you have the GOFAI (good old-fashioned AI -- logic and symbol-pushing) and machine learning crowds, who operate somewhere in the middle of theory and practice.
I'm sure I've left some areas out. I think how to unify a CS curriculum in light of such diverse interests is a hard problem worthy of study and debate, and dismissing the problem as "tenure" does not provide a useful frame for addressing the problems.
"professors work on such different areas that they can't even communicate"
Having worked in an Electrical Engineering department I'd say they had the same problem - it's not so much they couldn't communicate but the hyper specialization that academia encourages means that they just aren't interested in each others domains.
My major was Mechanical Engineering, which is perhaps even more of a grab-bag than CS. Within that one department, we had product design people who could have fit in in the art department, fluid mechanics people who were doing serious number crunching, and so on. For a major like that, maybe a potluck approach is the best you can hope for - offer students a taste of everything and let them decide.
I should also mention that the Intro to ME course was a very popular, hands-on course that let people build little mechanical gizmos with foam core and hot-melt glue guns. So perhaps that's not a bad way to introduce a subject like that.
The ML / probabilistic AI people think they're the latest and greatest, so they don't talk to anyone else, certainly not the GOFAI people, and especially not the statistics department.
Heh-heh.
Yesterday I was telling my co-worker that veered off-track in my CS education because I chose a mentor who practiced GOFAI, whereas my peers who worked on ML were much better oriented to a rewarding career in computer intelligence.
I'm confused when you say that "ML / probabilistic AI people" don't talk to the statistics department, though. ML is all about statistics. Maybe the statisticians don't talk to the ML researches because statisticians care more about manufacturing, medicine, and math then they care about AI.
I was confused too and still am. But the core ML course in my CS department gave a list of other "related courses" at the end of the course. Despite the fact that our Stats department has umpteen learning-related courses, not one of the courses on that list was in Stats.
In my experience the "not talking" definitely originates from the ML side, although there's probably some of it in both directions.
The first course in the Stats learning sequence had a tongue-in-cheek listing of differences between it and the CS learning course:
It's better to start a whole new field that is more applied.