For useful-tool AI, learn stuff from statistics and machine learning before making any further moves.
For self-improving AI, just don't do it as AI, FAI is not quite an AI problem, and anyway most techniques associated with "AI" don't work for FAI. Instead, learn fundamental math and computer science, to a good level -- that's my current best in-a-few-words advice for would-be FAI researchers.
Isn't every AI potentially a self-improving AI? All it takes is for the AI to come upon the insight "hey, I can build an AI to do my job better." I guess it requires some minimum amount of intelligence for such an insight to become likely, but my point is that one doesn't necessarily have to set out to build a self-improving AI, to actually build a self-improving AI.
A friend of mine is about to launch himself heavily into the realm of AI programming. The details of his approach aren't important; probabilities dictate that he is unlikely to score a major success. He's asked me for advice, however, on how to design a safe(r) AI. I've been pointing him in the right directions and sending him links to useful posts on this blog and the SIAI.
Do people here have any recommendations they'd like me to pass on? Hopefully, these may form the basis of a condensed 'warning pack' for other AI makers.
Addendum: Advice along the lines of "don't do it" is vital and good, but unlikely to be followed. Coding will nearly certainly happen; is there any way of making it less genocidally risky?