Learning how to create even a simple recommendation engine whose output is constrained by the values of its creators would be a large step forward and would help society today.

I think something showing how to do value learning on a small scale like this would be on topic. It might help to expose the advantages and disadvantages of algorithms like inverse reinforcement learning.

I also agree that, if there are more practical applications of AI safety ideas, this will increase interest and resources devoted to AI safety. I don't really see those applications yet, but I will look out for them. Thanks for bringing this to my attention.

it is demonstrably not the case in history that the fastest way to develop a solution is to ignore all practicalities and work from theory backwards

I don't have a great understanding of the history of engineering, but I get the impression that working from the theory backwards can often be helpful. For example, Turing developed the basics of computer science before sufficiently general computers existed.

My current impression is that solving FAI with a hypercomputer is a fundamentally easier problem that solving it with a bounded computer, and it's hard to say much about the second problem if we haven't made steps towards solving the first one. On the other hand, I do think that concepts developed in the AI field (such as statistical learning theory) can be helpful even for creating unbounded solutions.

AIXI showed that all the complexity of AGI lies in the practicalities, because the pure uncomputable theory is dead simple but utterly divorced from practice.

I would really like it if the pure uncomputable theory of Friendly AI were dead simple!

Anyway, AIXI has been used to develop more practical algorithms. I definitely approach many FAI problems with the mindset that we're going to eventually need to scale this down, and this makes issues like logical uncertainty a lot more difficult. In fact, Paul Christiano has written about tractable logical uncertainty algorithms, which is a form of "scaling down an intractable theory". But it helped to have the theory in the first place before developing this.

an ignore-all-practicalities theory-first approach is useless until it nears completion

Solutions that seem to work for practical systems might fail for superintelligence. For example, perhaps induction can yield acceptable practical solutions for weak AIs, but does not necessarily translate to new contexts that a superintelligence might find itself in (where it has to make pivotal decisions without training data for these types of decisions). But I do think working on these is still useful.

My current trajectory places the first AGI at 10 to 15 years out, and the first self-improving superintelligence shortly thereafter. Will MIRI have practical results in that time frame?

I consider AGI in the next 10-15 years fairly unlikely, but it might be worth having FAI half-solutions by then, just in case. Unfortunately I don't really know a good way to make half-solutions. I would like to hear if you have a plan for making these.