Sincere question : short of embarking on a PhD, is AI research something the average dev (even a very, very enthusiastic one) can reasonably hope to contribute to?

Sure, why not? The nice thing about this field is that you don't need a lot of specialized and expensive equipment to work. The biggest obstacle in that regard comes in if you're doing some kind of model training where GPU's are the best choice, and you need (a) super-fast GPU(s) to do model training in a reasonable period of time. So you might want to spend a few thousand dollars on a fairly nice GPU setup.

But wait... even that can be outsourced to "the cloud" given that AWS, GCP, etc. make GPU instances available on an on-demand basis. Yeah, you have to be careful of how much cloud spend you rack up, but the point is that you don't necessarily need a huge up-front investment.

Even beyond that, AWS make FPGA instances available, so if you think you can design your own hardware level logic for doing something more efficiently, you can try that out in the cloud.

And GPU's aside, depending on exactly what you're doing (remember, AI is more than just Deep Learning) maybe you can get by with a basic Spark cluster or Beowulf cluster running MPI. Again, you can do this in your home for pretty cheap, or do it in the cloud.

As for the knowledge / know-how... sure, you'd have to dig in and do some serious catching up (that's the phase I'm in now). But the nice thing is, so much of the output of this field is online and freely available. No, not everything is, but a ton of the important stuff shows up on arXiv.org, or in free journals like JMLR or JAIR. There's also tons of historical stuff available to help get context or to mine for ideas that were prematurely abandoned, etc. Look at the CSAIL archives, or the IJCAI archives. Also, a lot, if not all, of the NIPS papers are freely available. Same for ICML and some others. See:

http://proceedings.mlr.press/

http://jmlr.org/

http://jair.org

http://nips.cc

http://publications.csail.mit.edu/ai/pubs_browse.shtml

https://www.ijcai.org/past_proceedings

https://arxiv.org/list/cs.AI/recent

http://arxiv.org/list/cs.LG/recent

http://arxiv.org/list/cs.MA/recent

http://arxiv.org/list/cs.NE/recent

etc.

Also consider that a significant portion of the important software used in this field is open source and freely available. I won't even try to list the stuff that's out there, but would instead direct your attention to http://mloss.org or Wikipedia (or Google) for some options to explore.

And of course there are forums where you can seek assistance from others, including:

https://discuss.openai.com/

http://stats.stackexchange.com

http://ai.stackexchange.com

http://datascience.stackexchange.com

http://artificial.reddit.com

http://machinelearning.reddit.com

etc.

The other thing that comes up is the need to know some maths stuff. Luckily the level of maths typically used in this field isn't that bad. You're not typically looking at needing Real Analysis, Abstract Algebra, Galois Theory, Topology, etc. A lot of AI/ML can be understood (from a mathematical POV) with just Calculus and Linear Algebra.

And if you don't already know those subjects, there are tons of online resources to help one learn them.

An interesting thing about AI/ML is that it's a very empirical subject. Not that there is no theory, but by and large you can come up with an idea for an approach to cognition / pattern matching that you think might work, and just go implement it, test it against existing approaches, and know if you've accomplished something.

Note that I'm not saying any of this is easy. Just that I think it's possible for somebody who's really motivated.