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Other academics, for example those doing "stuff going way beyond where industry is focused today" as the author explicitly states, can safely ignore it.
Translation: Universities should continue on the path to becoming the research arm of industry. Academics should alter what they do and how they think, in order to better suit industry. Academic research does not have value or merit of its own, outside of its usefulness to industry. Impact on the world happens only in an industrial context.
Alternatively: if you are doing work that attempts to have industry relevance you should have some idea of what problems are actually relevant to industry. In particular, just because you think something is an interesting and challenging problem that just has to be affecting industry players does not mean it actually is. It may have been solved already, or you may have made some poor assumptions on conceiving the problem which, if corrected, make the problem disappear entirely (perhaps replaced by a different one that would've been a more valuable research target).
If you're trying to do forward-looking invent things that aren't even a twinkle in industry's eye yet, that's absolutely fine too, and the author explicitly calls out that the only important thing here is to recognize when your work isn't likely to be applicable in the near term. Nowhere does Matt state that this makes this sort of research less valuable, and honestly in many cases academia is the only place it can reasonably happen due to funding incentives.
> Industry is far more collaborative and credit is widely shared. This couldn't be farther from the truth. Your idea's are generally credited to the company, which in turn is credited to the CEO or some other high up. On collaboration, it's only more collaborative within a given company , and not always even then. Between companies it's outright hostile to collaboration by definition.
> It drives me insane to see papers that claim that some problem is "unsolved" when most of the industry players have already solved it, but they didn't happen to write an NSDI or SIGCOMM paper about it.
I've seen many examples of industry "solutions" that aren't documented, aren't published, and aren't even validated. There's a place for papers like these. I'm not quite your typical CS researcher (I do applied math and software for medical imaging), so YMMV, but I think this criticism is too harsh.
When you're telling other people what they should do, you're already lost. I also don't feel academia is obliged to any particular promise of delivery, but that's kind of independent.
Instead it's saying roughly: much of the work coming out of academia attempts solve problems applicable to industry, but in actuality industry is not actually suffering from the problems solved (either because underlying assumptions are wrong and industry is plagued by a different problem or because the problem has already be adequately solved by existing work).
I know you Silicon Valley people think that farmers are a bunch of hicks and an easy target to be disrupted. No f-ing way. Cornell University has many departments that are good, but when I look at the agriculture and vet school they are beyond anybody else. Ag schools do research which is relevant to the technological and business problems of their industry. They do plenty of work on genetic engineering, chemistry an…
This is all much different from a software researcher who can just work from his/her lab (or even bedroom) at all times. It's easy to lose sight of the greater industry when you don't need its (direct) help to complete your research.
Now, I don't think all academic research has to be relevant to industry. I'm getting kind of sick of this tactic, where this fake concession is made before going on about how academia isn't designed well enough to deliver to industry. If authors of articles like this didn't care about the efficiency of academia to deliver them research, what's the big deal if there are a bunch of people somewhere running around in ci…
"Mostly I'm involved in mobile, wireless, and systems, and in all three of these subfields I see plenty of academic work that tries to solve problems relevant to industry , but often gets it badly wrong." If you want to work on problems irrelevant to industry, go nuts. If you want to work on problems relevant to the industry, it helps to double-check that your problems are relevant to the industry. The desire to work…
I know you Silicon Valley people think that farmers are a bunch of hicks and an easy target to be disrupted. No f-ing way. Cornell University has many departments that are good, but when I look at the agriculture and vet school they are beyond anybody else. Ag schools do research which is relevant to the technological and business problems of their industry. They do plenty of work on genetic engineering, chemistry an…
Cornell? It's pronounced "colonel" and it's the highest rank in the military.
Over time, fields of study become industrialized. There was a time when doing research in computer vision, machine learning, and speech processing was risky because the field was new, difficult to enter, and the prospects for commercialization were slim. That time has passed. Those 20 people working at Google are the people that helped that time pass. One could argue that the place for this work is now in industry - the motivations are all right and the resources and data are aligned to carry the work forward at a rapid pace.
This happens in other fields. For example, there's some word on the street that DARPA is going to stop funding so much basic research into applied robotics. Industry, they say, has got this covered. You can argue that they're right. The commercial sector is starting to get real thirsty for robots. Amazon talks about automated drone delivery. Everyone talks about self driving cars. The military wants to buy automated planes as a purchase, not as a research project. The time for basic research, it seems, is over.
As far as I can tell, this happened with systems about fifteen years ago, so the academic activity you see in systems is what is left over after all of the researchers that could do things moved into applying their research in industry. You no longer need to have weird hair and be buried in a basement to think about 20 computers talking to each other in parallel - you can go work at any technology company and think about two million computers talking to each other in parallel, and get paid two orders of magnitude more money. So the people doing systems research in academia are the people that cannot take their systems research into industry. If they could get internships, they would, and then they would get jobs. They haven't.