I think this is a good point; you may also be interested in Michelle's post about beneficiary groups, my comment about beneficiary subgroups, and Michelle's follow-up about finding more effective causes.

Thanks Tobias.

In a hard / unexpected takeoff scenario, it's more plausible that we need to get everything more or less exactly right to ensure alignment, and that we have only one shot at it. This might favor HRAD because a less principled approach makes it comparatively unlikely that we get all the fundamentals right when we build the first advanced AI system.

FWIW, I'm not ready to cede the "more principled" ground to HRAD at this stage; to me, it seems like the distinction is more about which aspects of an AI system's behavior we're specifying manually, and which aspects we're setting it up to learn. As far as trying to get everything right the first time, I currently favor a corrigibility kind of approach, as I described in 3c above -- I'm worried that trying to solve everything formally ahead of time will actually expose us to more risk.

Thanks for these thoughts. (Your second link is broken, FYI.)

On empirical feedback: my current suspicion is that there are some problems where empirical feedback is pretty hard to get, but I actually think we could get more empirical feedback on how well HRAD can be used to diagnose and solve problems in AI systems. For example, it seems like many AI systems implicitly do some amount of logical-uncertainty-type reasoning (e.g. AlphaGo, which is really all about logical uncertainty over the result of expensive game-tree computations) -- maybe HRAD could be used to understand how those systems could fail?

I'm less convinced that the "ignored physical aspect of computation" is a very promising direction to follow, but I may not fully understand the position you're arguing for.

My guess is that the capability is extremely likely, and the main difficulties are motivation and reliability of learning (since in other learning tasks we might be satisfied with lower reliability that gets better over time, but in learning human preferences unreliable learning could result in a lot more harm).

Thanks for this suggestion, Kaj -- I think it's an interesting comparison!

I am very bullish on the Far Future EA Fund, and donate there myself. There's one other possible nonprofit that I'll publicize in the future if it gets to the stage where it can use donations (I don't want to hype this up as an uber-solution, just a nonprofit that I think could be promising).

I unfortunately don't spend a lot of time thinking about individual donation opportunities, and the things I think are most promising often get partly funded through Open Phil (e.g. CHAI and FHI), but I think diversifying the funding source for orgs like CHAI and FHI is valuable, so I'd consider them as well.

I think there's something to this -- thanks.

To add onto Jacob and Paul's comments, I think that while HRAD is more mature in the sense that more work has gone into solving HRAD problems and critiquing possible solutions, the gap seems much smaller to me when it comes to the justification for thinking HRAD is promising vs justification for Paul's approach being promising. In fact, I think the arguments for Paul's work being promising are more solid than those for HRAD, despite it only being Paul making those arguments -- I've had a much harder time understanding anything more nuanced than the basic case for HRAD I gave above, and a much easier time understanding why Paul thinks his approach is promising.

My perspective on this is a combination of “basic theory is often necessary for knowing what the right formal tools to apply to a problem are, and for evaluating whether you're making progress toward a solution” and “the applicability of Bayes, Pearl, etc. to AI suggests that AI is the kind of problem that admits of basic theory.” An example of how this relates to HRAD is that I think that Bayesian justifications are useful in ML, and that a good formal model of rationality in the face of logical uncertainty is likely to be useful in analogous ways. When I speak of foundational understanding making it easy to design the right systems, I’m trying to point at things like the usefulness of Bayesian justifications in modern ML. (I’m unclear on whether we miscommunicated about what sort of thing I mean by “basic insights”, or whether we have a disagreement about how useful principled justifications are in modern practice when designing high-reliability systems.)

Just planting a flag to say that I'm thinking more about this so that I can respond well.

Thanks Nate!

The end goal is to prevent global catastrophes, but if a safety-conscious AGI team asked how we’d expect their project to fail, the two likeliest scenarios we’d point to are "your team runs into a capabilities roadblock and can't achieve AGI" or "your team runs into an alignment roadblock and can easily tell that the system is currently misaligned, but can’t figure out how to achieve alignment in any reasonable amount of time."

This is particularly helpful to know.

We worry about "unknown unknowns", but I’d probably give them less emphasis here. We often focus on categories of failure modes that we think are easy to foresee. As a rule of thumb, when we prioritize a basic research problem, it’s because we expect it to help in a general way with understanding AGI systems and make it easier to address many different failure modes (both foreseen and unforeseen), rather than because of a one-to-one correspondence between particular basic research problems and particular failure modes.

Can you give an example or two of failure modes or "categories of failure modes that are easy to foresee" that you think are addressed by some HRAD topic? I'd thought previously that thinking in terms of failure modes wasn't a good way to understand HRAD research.

As an example, the reason we work on logical uncertainty isn’t that we’re visualizing a concrete failure that we think is highly likely to occur if developers don't understand logical uncertainty. We work on this problem because any system reasoning in a realistic way about the physical world will need to reason under both logical and empirical uncertainty, and because we expect broadly understanding how the system is reasoning about the world to be important for ensuring that the optimization processes inside the system are aligned with the intended objectives of the operators.

I'm confused by this as a follow-up to the previous paragraph. This doesn't look like an example of "focusing on categories of failure modes that are easy to foresee," it looks like a case where you're explicitly not using concrete failure modes to decide what to work on.

“how do we ensure the system’s cognitive work is being directed at solving the right problems, and at solving them in the desired way?”

I feel like this fits with the "not about concrete failure modes" narrative that I believed before reading your comment, FWIW.

Thanks!