Artificial intelligence capabilities research is aimed at making computer systems more intelligent — able to solve a wider range of problems more effectively and efficiently. We can distinguish this from research specifically aimed at making AI systems at various capability levels safer, or more “robust and beneficial.” In this post, I distinguish three kinds of direct research that might be thought of as “AI safety” work: safety engineering, target selection, and alignment theory.

Imagine a world where humans somehow developed heavier-than-air flight before developing a firm understanding of calculus or celestial mechanics. In a world like that, what work would be needed in order to safely transport humans to the Moon?

In this case, we can say that the main task at hand is one of engineering a rocket and refining fuel such that the rocket, when launched, accelerates upwards and does not explode. The boundary of space can be compared to the boundary between narrowly intelligent and generally intelligent AI. Both boundaries are fuzzy, but have engineering importance: spacecraft and aircraft have different uses and face different constraints.

Paired with this task of developing rocket capabilities is a safety engineering task. Safety engineering is the art of ensuring that an engineered system provides acceptable levels of safety. When it comes to achieving a soft landing on the Moon, there are many different roles for safety engineering to play. One team of engineers might ensure that the materials used in constructing the rocket are capable of withstanding the stress of a rocket launch with significant margin for error. Another might design escape systems that ensure the humans in the rocket can survive even in the event of failure. Another might design life support systems capable of supporting the crew in dangerous environments.

A separate important task is target selection, i.e., picking where on the Moon to land. In the case of a Moon mission, targeting research might entail things like designing and constructing telescopes (if they didn’t exist already) and identifying a landing zone on the Moon. Of course, only so much targeting can be done in advance, and the lunar landing vehicle may need to be designed so that it can alter the landing target at the last minute as new data comes in; this again would require feats of engineering.

Beyond the task of (safely) reaching escape velocity and figuring out where you want to go, there is one more crucial prerequisite for landing on the Moon. This is rocket alignment research, the technical work required to reach the correct final destination. We’ll use this as an analogy to illustrate MIRI’s research focus, the problem of artificial intelligence alignment.

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Andrew Critch, one of the new additions to MIRI’s research team, has taken the opportunity of MIRI’s winter fundraiser to write on his personal blog about why he considers MIRI’s work important. Some excerpts:

Since a team of CFAR alumni banded together to form the Future of Life Institute (FLI), organized an AI safety conference in Puerto Rico in January of this year, co-authored the FLI research priorities proposal, and attracted $10MM of grant funding from Elon Musk, a lot of money has moved under the label “AI Safety” in the past year. Nick Bostrom’s Superintelligence was also a major factor in this amazing success story.

A lot of wonderful work is being done under these grants, including a lot of proposals for solutions to known issues with AI safety, which I find extremely heartening. However, I’m worried that if MIRI doesn’t scale at least somewhat to keep pace with all this funding, it just won’t be spent nearly as well as it would have if MIRI were there to help.

We have to remember that AI safety did not become mainstream by a spontaneous collective awakening. It was through years of effort on the part of MIRI and collaborators at FHI struggling to identify unknown unknowns about how AI might surprise us, and struggling further to learn to explain these ideas in enough technical detail that they might be adopted by mainstream research, which is finally beginning to happen.

But what about the parts we’re wrong about? What about the sub-problems we haven’t identified yet, that might end up neglected in the mainstream the same way the whole problem was neglected 5 years ago? I’m glad the AI/ML community is more aware of these issues now, but I want to make sure MIRI can grow fast enough to keep this growing field on track.

Now, you might think that now that other people are “on the issue”, it’ll work itself out. That might be so.

But just because some of MIRI’s conclusions are now being widely adopted widely doesn’t mean its methodology is. The mental movement

“Someone has pointed out this safety problem to me, let me try to solve it!”

is very different from

“Someone has pointed out this safety solution to me, let me try to see how it’s broken!”

And that second mental movement is the kind that allowed MIRI to notice AI safety problems in the first place. Cybersecurity professionals seem to carry out this movement easily: security expert Bruce Schneier calls it the security mindset. The SANS institute calls it red teaming. Whatever you call it, AI/ML people are still more in maker-mode than breaker-mode, and are not yet, to my eye, identifying any new safety problems.

I do think that different organizations should probably try different approaches to the AI safety problem, rather than perfectly copying MIRI’s approach and research agenda. But I think breaker-mode/security mindset does need to be a part of every approach to AI safety. And if MIRI doesn’t scale up to keep pace with all this new funding, I’m worried that the world is just about to copy-paste MIRI’s best-2014-impression of what’s important in AI safety, and leave behind the self-critical methodology that generated these ideas in the first place… which is a serious pitfall given all the unknown unknowns left in the field.

See our funding drive post to help contribute or to learn more about our plans. For more about AI risk and security mindset, see also Luke Muehlhauser’s post on the topic.