(1) Things Executive!Nate will do differently from Researcher!Nate? Or things Nate!MIRI will do differently from Luke!MIRI? For the former, I’ll be thinking lots more about global coordination & engaging with interested academics etc, and lots less about specific math problems. For the latter, the biggest shift is probably going to be something like “more engagement with the academic mainstream,” although it’s a bit hard to say: Luke probably would have pushed in that direction too, after growing the research team a bit. (I have a lot of opportunities available to me that weren’t available to Luke at this time last year.)
(2) The old SIAI definitely made some obvious mistakes; see e.g. Holden Karnofsky’s 2012 critique. Luke tried to transfer a number of the lessons learned to me, but it remains to be seen whether I actually learned them :-) The concrete list includes things like (a) constantly drive to systematize, automate, and outsource the busywork; (b) always attack the biggest constraint (by contrast, most people seem to have a default mode of “try and do everything that meets a certain importance level”); (c) put less emphasis on explicit models that you’ve built yourself an more emphasis on advice from others who have succeeded in doing something similar to what you’re trying to do.
(3) MIRI played a pretty big role in getting long-term AI alignment issues onto the world stage. There are lots and lots of things I’ve learned from that particular success. Perhaps the biggest is “don’t disregard intellectual capital.”
What is the top thing you think you’ll do differently now that you’re Executive Director?
What do you think is the biggest mistake MIRI has made in it’s past? How have you learned from it?
What do you think has been the biggest success MIRI has had? How have you learned from that?
(1) Things Executive!Nate will do differently from Researcher!Nate? Or things Nate!MIRI will do differently from Luke!MIRI? For the former, I’ll be thinking lots more about global coordination & engaging with interested academics etc, and lots less about specific math problems. For the latter, the biggest shift is probably going to be something like “more engagement with the academic mainstream,” although it’s a bit hard to say: Luke probably would have pushed in that direction too, after growing the research team a bit. (I have a lot of opportunities available to me that weren’t available to Luke at this time last year.)
(2) The old SIAI definitely made some obvious mistakes; see e.g. Holden Karnofsky’s 2012 critique. Luke tried to transfer a number of the lessons learned to me, but it remains to be seen whether I actually learned them :-) The concrete list includes things like (a) constantly drive to systematize, automate, and outsource the busywork; (b) always attack the biggest constraint (by contrast, most people seem to have a default mode of “try and do everything that meets a certain importance level”); (c) put less emphasis on explicit models that you’ve built yourself an more emphasis on advice from others who have succeeded in doing something similar to what you’re trying to do.
(3) MIRI played a pretty big role in getting long-term AI alignment issues onto the world stage. There are lots and lots of things I’ve learned from that particular success. Perhaps the biggest is “don’t disregard intellectual capital.”