> Lesson: Practical problems, taken seriously, can result in some of the most important problems in pure research, and some of the progress in pure research can help get solutions to some practical problems. The motivation from pressing practical problems can help drive the research in both pure and applied research.
I'm pretty sure I explicitly agreed that this is often the case in my original post, so we must be talking past one another :)
What I'm arguing for is basically just academic freedom: the freedom of faculty and students to make choices about where they should resarch agenda. As your extensive history demonstrates, THIS APPROACH WORKS! All of those people chose to engage with industrial because it made sense for their research agenda!
More importantly, we can come up with an equally lengthy wall of text detailing accomplishments that would not have been possible without the freedom to work on things that industry isn't all hot and bothered about. E.g., neural nets until about 5 years ago!
And an even lengthier wall of text describing silly research agendas that only existed because of industry hype (AOP anyone?)
Industry collaboration can be a tremendous impetus. However, it can also be a distraction from more important problems or even an impetus to focus on silly problems. Professors and students should be incentivized and encouraged to do good research; industrial collaboration can sometimes be a useful tool, but it is a means, not an end.
Finally, IMO, the central premise of your argument (that there's not enough collaboration) is not factually accurate in the current climate. Read the proceeds of any major AI conference. Filter out papers written at top universities. Count the number of papers with vs. without an industrial collaborator named in the acks or even in the author list. Failure to collaborate isn't a failing of modern mainstream AI research.
I never tried to constrain "freedom" in research. Freedom in research is crucial: With a good researcher, often only they have a good sense of the promise of their research direction. And, they are the one making a bet: If their research is soon good, then, modulo academic politics, they make progress in their academic career, e.g., maybe get to upgrade their 20 year old used Mazda to a 10 year old used Toyota and celebrate with a toast of tap water!!!
If current academic AI research is too close to non-academic problems, okay, I can believe that but see little downside since I have no respect for 90+% of current AI work anyway.
Net, contact with non-academic problems is crucial for STEM fields but with bad work can be abused. Of course it can be abused, special case of the general situation that nearly anything can be abused.
I spent a lot of time in STEM field academics: My considered, solid, well informed opinion is that there is far too little contact with important non-academic problems. E.g., when I went from Director of Operations Research at FedEx to graduate school in applied math, I brought with me a nice collection of important practical problems. In casual conversations, as I described some of those problems, even very pure research profs took detailed notes furiously. When I was an applied math prof in a B-school and MBA program, there were nearly no people from business in the halls with pressing problems looking for solutions, and that situation was really bad for the the business people, the students, the faculty, faculty research, and the B-school.
The suspicion has to be strong that if a research-teaching hospital were run like a B-school, then the physicians and researchers would be off studying the possibilities of silicon-based life on the planet Faraway, no one would know even how to dress a skinned knee, there would be no progress on any of the major, pressing medical problems, e.g., heart disease, cancer, and no one would want to go to a hospital no matter how badly they hurt.
> I never tried to constrain "freedom" in research. Freedom in research is crucial
Well then, I think we're violently agreeing. However, a couple of observations.
> e.g., maybe get to upgrade their 20 year old used Mazda to a 10 year old used Toyota and celebrate with a toast of tap water!!!
Here is CMU's dean on what happens to faculty with successful AI/ML research agendas: "How to retain people who are worth tens of millions of dollars to other organizations is causing my few remaining hairs to fall out".
I didn't realize how expensive used Toyotas have gotten...
>...applied math
I'll again reiterate that CS and especially AI have a completely different culture.
Also, this sentence seems to somehow undermine your entire thesis:
> If current academic AI research is too close to non-academic problems, okay, I can believe that but see little downside since I have no respect for 90+% of current AI work anyway.
I'm pretty sure I explicitly agreed that this is often the case in my original post, so we must be talking past one another :)
What I'm arguing for is basically just academic freedom: the freedom of faculty and students to make choices about where they should resarch agenda. As your extensive history demonstrates, THIS APPROACH WORKS! All of those people chose to engage with industrial because it made sense for their research agenda!
More importantly, we can come up with an equally lengthy wall of text detailing accomplishments that would not have been possible without the freedom to work on things that industry isn't all hot and bothered about. E.g., neural nets until about 5 years ago!
And an even lengthier wall of text describing silly research agendas that only existed because of industry hype (AOP anyone?)
Industry collaboration can be a tremendous impetus. However, it can also be a distraction from more important problems or even an impetus to focus on silly problems. Professors and students should be incentivized and encouraged to do good research; industrial collaboration can sometimes be a useful tool, but it is a means, not an end.
Finally, IMO, the central premise of your argument (that there's not enough collaboration) is not factually accurate in the current climate. Read the proceeds of any major AI conference. Filter out papers written at top universities. Count the number of papers with vs. without an industrial collaborator named in the acks or even in the author list. Failure to collaborate isn't a failing of modern mainstream AI research.