Error-correcting codes work by running some algorithm to decode potentially-corrupted data. But what if the algorithm might also have been corrupted? One approach to dealing with this is triple modular redundancy, in which three copies of the algorithm each do the computation and take the majority vote on what the output should be. But this still creates a single point of failure—the part where the majority voting is implemented. Maybe this is fine if the corruption is random, because the voting algorithm can constitute a very small proportion of the total code. But I'm most interested in the case where the corruption happens adversarially—where the adversary would home in on the voting algorithm as the key thing to corrupt.

After a quick search, I can't find much work on this specific question. But I want to speculate on what such an "error-correcting algorithm" might look like. The idea of running many copies of it in parallel seems solid, so that it's hard to corrupt a majority at once. But there can't be a single voting algorithm (or any other kind of "overseer") between those copies and the output channel, because that overseer might itself be corrupted. Instead, you need the majority of the copies to be able to "overpower" the few corrupted copies to control the output channel via some process that isn't mediated by a small easily-corruptible section of code.

The viability of some copies "overpowering" other copies will depend heavily on the substrate on which they're running. For example, if all the copies are running on different segments of a Universal Turing Machine tape, then a corrupted copy could potentially just loop forever and prevent the others from answering. So in order to make error-correcting algorithms viable we may need a specific type of Universal Turing Machine which somehow enforces parallelism. Then you need some process by which copies that agree on their outputs can "merge" together to form a more powerful entity; and by which entities that disagree can "fight it out". At the end there should be some way for the most powerful entity to control the output channel (which isn't accessible while conflict is still ongoing).

The punchline is that we seem to have built up a kind of model of "agency" (and, indeed, almost a kind of politics) from these very basic assumptions. Perhaps there are other ways to create such error-correcting algorithms. If so, I'd be very interested in hearing about them. But I increasingly suspect that agency is a fundamental concept which will emerge in all sorts of surprising places, if only we know how to look for it.

The people I instinctively checked after reading this:

• Pichai: 5'11
• Gates: 5'10
• Ballmer: 6'5
• I got conflicting estimates for Jobs and Nadella

See my post on pessimization.

A few quick comments, on the same theme as but mostly unrelated to the exchange so far:

1. I'm not very sold on "cares about xrisk" as a key metric for technical researchers. I am more interested in people who want to very deeply understand how intelligence works (whether abstractly or in neural networks in particular). I think the former is sometimes a good proxy for the latter but it's important not to conflate them. See this post for more.
2. Having said that, I don't get much of a sense that many MATS scholars want to deeply understand how intelligence works. When I walked around the poster showcase at the most recent iteration of MATS, a large majority of the projects seemed like they'd prioritized pretty "shallow" investigations. Obviously it's hard to complete deep scientific work in three months but at least on a quick skim I didn't see many projects that seemed like they were even heading in that direction. (I'd cite Tom Ringstrom as one example of a MATS scholar who was trying to do deep and rigorous work, though I also think that his core assumptions are wrong.)
3. As one characterization of an alternative approach: my intership with Owain Evans back in 2017 consisted of me basically sitting around and thinking about AI safety for three months. I had some blog posts as output but nothing particularly legible. I think this helped nudge me towards thinking more deeply about AI safety subsequently (though it's hard to assign specific credit).
4. There's an incentive alignment problem where even if mentors want scholars to spend their time thinking carefully, the scholars' careers will benefit most from legible projects. In my most recent MATS cohort I've selected for people who seem like they would be happy to just sit around and think for the whole time period without feeling much internal pressure to produce legible outputs. We'll see how that goes.

At some point I recall thinking to myself "huh, LessWrong is really having a surge of good content lately". Then I introspected and realized that about 80% of that feeling was just that you've been posting a lot.

FWIW, in case this is helpful, my impression is that:

• It is accurate to describe Wei as doing a "charge of the hobby-horse" in his initial comment, and this should be considered a mild norm violation. I'm also surprised and a bit disappointed that it got so many upvotes.
• By the time that Tsvi announced the ban, Wei had already acknowledged that his original comments had been partly based on a misunderstanding. In my culture, I would expect more of an apology for doing so than the "ok...but to be fair" follow-up Wei actually gave. However, the phrase "Also, another part of my motivation is still valid and I think it would be interesting to try to answer" is a clear enough acknowledgement of a distinct line of inquiry that I no longer consider that comment to be a continuation of the "charge of the hobby-horse".
• Tsvi banning Wei for "grossly negligent reading comprehension" after Wei had acknowledged that he was mistaken seems like a mild norm violation. It wouldn't have been a norm violation if Wei's comment hadn't made that acknowledgement; however, it would have been a stronger norm violation if Wei's comment had included an actual apology.

This has pretty low argumentative/persuasive force in my mind.

Note that my comment was not optimized for argumentative force about the overarching point. Rather, you asked how they "can" still benefit the world, so I was trying to give a central example.

In the second half of this comment I'll give a couple more central examples of how virtues can allow people to avoid the traps you named. You shouldn't consider these to be optimized for argumentative force either, because they'll seem ad-hoc to you. However, they might still be useful as datapoints.

Figuring out how to describe the underlying phenomenon I'm pointing at in a compelling, non-ad-hoc way is one of my main research focuses. The best I can do right now is to say that many of the ways in which people produce outcomes which are harmful (by their own lights) seem to arise from a handful of underlying dynamics. I call this phenomenon pessimization. One way in which I'm currently thinking about virtues is as a set of cognitive tools for preventing pessimization. As one example, kindness and forgiveness help to prevent cycles of escalating conflict with others, which is a major mechanism by which people's values get pessimized. This one is pretty obvious to most people; let me sketch out some less obvious mechanisms below.

what if someone isn't smart enough to come up with a new line of illegible research, but does see some legible problem with an existing approach that they can contribute to? What would cause them to avoid this?

This actually happened to me: when I graduated from my masters I wasn't cognitively capable of coming up with new lines of illegible alignment research, in part because I was too status-seeking. Instead I went to work at DeepMind, and ended up spending a lot of my time working on RLHF, which is a pretty central example of a "legible" line of research.

However, I also wasn't cognitively capable of making much progress on RLHF, because I couldn't see how it addressed the core alignment problem, and so it didn't seem fundamental enough to maintain my interest. Instead I spent most of my time trying to understand the alignment problem philosophically (resulting in this sequence) at the expense of my promotion prospects.

In this case I think I had the virtue of deep curiosity, which steered my attention towards illegible problems even though my top-down plan was to contribute to alignment by doing RLHF research. These days, whatever you might think of my research, few people complain that it's too legible.

There are other possible versions of me who had that deep curiosity but weren't smart enough to have generated a research agenda like my current one; however, I think they would still have left DeepMind, or at least not been very productive on RLHF.

And even the hypothetical virtuous person who starts doing illegible research on their own, what happens when other people catch up to him and the problem becomes legible to leaders/policymakers? How would they know to stop working on that problem and switch to another problem that is still illegible?

When a field becomes crowded, there's a pretty obvious inference that you can make more progress by moving to a less crowded field. I think people often don't draw that inference because moving to a less crowded field loses them prestige, is emotionally/financially risky, etc. Virtues help remove those blockers.

though I think you don't need to invoke knightian uncertainty. I think it's simply enough to model there being a very large attack surface combined with a more intelligent adversary.

One of the problems I'm pointing to is that you don't know what the attack surface is. This puts you in a pretty different situation than if you have a known large attack surface to defend, even against a smarter adversary (e.g. the whole length of a border; or every possible sequence of Go moves).

Separately, I may be being a bit sloppy by using "Knightian uncertainty" as a broad handle for cases where you have important "unknown unknowns", aka you don't even know what ontology to use. But it feels close enough that I'm by default planning to continue describing the research project outlined above as trying to develop a theory of Knightian uncertainty in which Bayesian uncertainty is a special case.

I also have a short story about (some aspects of) paranoia from the inside.