How do (STEM) professors keep up with the latest tech or development in order to publish research? From an outsider/student's view, STEM professors are:

not taking courses
mostly not doing things outside of their research comfort zones
not (?) studying up much except for their research, certainly not doing exercises from the back of a book and looking up solutions somewhere
some graduated 40 years ago or even more.
teaching the same course for past 20 years, which let's face it, is sort of like teaching history with numbers.
not visibly getting taught by their research students or anyone else for that matters.

How is it that a lot of the professors are publishing cutting edge research (especially in STEM) involving the latest tech and gadgets? If I am not reading the news, I wouldn't know what Apple is doing right now let alone develop an algorithm that mimics what their Face Recognition AI is doing.
How is it possible that a professor with a PhD in analog circuit back in the 70s could do research in today's massive microchip fabrication process when a tremendous amount of information/material is needed to get up to speed both in terms of theory and practical knowledge? Do these professors have fabrication facilities in their garage or something? How would they know without being in industry or taking a course?
How is it possible that a professor with a PhD in numerical optimization from the 80s where the latest and the great was quasi-Newton method is doing research on the latest deep learning algorithm? How can this person even be proficient at this? Are they taking courses on Coursera or something?
How do you do it?
In short, your premises are wrong.

Professors read papers to learn.
Professors meet with their peers, both at conferences and informally, and learn from them.
Professors do learn from their research students.

These are slow methods of learning, but over many years they add up.
Further, in fast-moving fields of research, nobody &quot;keeps up&quot; in the sense of knowing everything that is going on.  It isn't necessary to know everything that is already known to create new knowledge.