Since early 2013, MIRI’s core goal has been to help create a new field of research devoted to the technical challenges of getting good outcomes from future AI agents with highly general capabilities, including the capability to recursively self-improve.2
Launching a new field has been a team effort. In 2013, MIRI decided to focus on its comparative advantage in defining open problems and making technical progress on them. We’ve been fortunate to coordinate with other actors in this space — FHI, CSER, FLI, and others — who have leveraged their comparative advantages in conducting public outreach, building coalitions, pitching the field to grantmakers, interfacing with policymakers, and more.3
MIRI began 2014 with several open problems identified, and with some progress made toward solving them, but with very few people available to do the work. Hence, most of our research program effort in 2014 was aimed at attracting new researchers to the field and making it easier for them to learn the material and contribute. This was the primary motivation for our new technical agenda overview, the MIRIx program, our new research guide, and more (see below). Nick Bostrom’s Superintelligence was also quite helpful for explaining why this field of research should exist in the first place.
Today the field is much larger and healthier than it was at the beginning of 2014. MIRI now has four full-time technical researchers instead of just one. Around 85 people have attended one or more MIRIx workshops. There are so many promising researchers who have expressed interest in our technical research that ~25 of them have already confirmed interest and availability to attend a MIRI introductory workshop this summer, and this mostly doesn’t include people who have attended past MIRI workshops, nor have we sent out all the invites yet. Moreover, there are now several researchers we know who are plausible MIRI hires in the next 1-2 years.
I am extremely grateful to MIRI’s donors, without whom this progress would have been impossible.
The rest of this post provides a more detailed summary of our activities in 2014.
- This year’s annual review is shorter than last year’s 5-part review of 2013, in part because 2013 was an unusually complicated focus-shifting year, and in part because, in retrospect, last year’s 5-part review simply took more effort to produce than it was worth. Also, because we recently finished switching to accrual accounting, I can now more easily provide annual reviews of each calendar year rather than of a March-through-February period. As such, this review of calendar year 2014 will overlap a bit with what was reported in the previous annual review (of March 2013 through February 2014). ↩
- Clearly there are forecasting and political challenges as well, and there are technical challenges related to ensuring good outcomes from nearer-term AI systems, but MIRI has chosen to specialize in the technical challenges of aligning superintelligence with human interests. See also: Friendly AI research as effective altruism and Why MIRI? ↩
- Obviously, the division of labor was more complex than I’ve described here. For example, FHI produced some technical research progress in 2014, and MIRI did some public outreach. ↩