David J. Balan once proposed that there are two kinds of “no evidence”:

1. There have been lots of studies directly on this point which came back with the result that the hypothesis is false.
2. There is no evidence because there are few or no relevant studies.

I propose that there are three kinds of “no evidence”:

1. The hypothesis has never been studied.
2. There are studies, the studies failed to find supporting evidence, but they wouldn’t have found supporting evidence even if the hypothesis were true.
3. There are studies, the studies should have found supporting evidence if the hypothesis were true, and they didn’t.

Example of type 1: A 2003 literature review found that there were no studies¹ showing that parachutes could prevent injury when jumping out of a plane.

Example of type 2: In 2018, there was finally a randomized controlled trial² on the effectiveness of parachutes, and it found no difference between the parachute group and the control group. However, participants only jumped from a height of 0.6 meters (~2 feet). I don’t know about you, but this result does not make me want to jump out of a plane without a parachute.

Like in the parachute example, you see type-2 “no evidence” whenever the conditions of a study don’t match the real-world environment. You also see type-2 “no evidence” when an experiment is underpowered. Say you want to test the hypothesis that boys are taller than girls. So you go find your niece Sally and your neighbor’s son James and it turns out Sally is an inch taller than James. Your methodology was valid—you can indeed test the hypothesis by finding some people and measuring their heights—but your sample size was too small.

(The difference between type 2 and type 3 can be a matter of degree. The more powerful a study is, the stronger its “no evidence” if it fails to find an effect.)

Notes

Some people (including me) have argued that altruists often benefit from leveraging their investments. Recently, it has become easier to use leverage thanks to the emergence of return stacked funds.

This is not financial advice.

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This research piece is not as rigorous or polished as usual. I wrote it quickly in a stream-of-consciousness style, which means it’s more reflective of my actual reasoning process.

My understanding of HIIT (high-intensity interval training) as of a week ago:

1. VO2max is the best fitness indicator for predicting health and longevity.
2. HIIT, especially long-duration intervals (4+ minutes), is the best way to improve VO2max.
3. Intervals should be done at the maximum sustainable intensity.

I now believe those are all probably wrong.

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An if-then commitment is a framework for responding to AI risk: “If an AI model has capability X, then AI development/deployment must be halted until mitigations Y are put in place.”

As an extension of this approach, we should consider retroactive if-then commitments. We should behave as if we wrote if-then commitments a few years ago, and we should commit to implementing whatever mitigations we would have committed to back then.

Imagine how an if-then commitment might have been written in 2020:

Pause AI development and figure out mitigations if:

• AI exhibits what looks like deceptive or misaligned behavior, or feigns alignment (1, 1b, 2)
• AI breaks out of containment in a toy example
• AI finds a real-world zero-day vulnerability
• AI qualifies for Mensa¹
• AI exhibits some degree of agentic capabilities
• AI writes malware

Well, AI models have now done or nearly-done all of those things.

We don’t know what mitigations are appropriate, so AI companies should pause development until (at a minimum) AI safety researchers agree on what mitigations are warranted, and those mitigations are then fully implemented.

(You could argue about whether AI really hit those capability milestones, but that doesn’t particularly matter. You need to pause and/or restrict development of an AI system when it looks potentially dangerous, not definitely dangerous.)

Notes

(I write listicles now)

(there are only 7 eligible high-protein breakfast cereals, so the ones at the bottom are still technically among the 7 best even though they’re not good)

If you search the internet, you can find rankings of the best “high-protein” breakfast cereals. But most of the entries on those lists don’t even have that much protein. I don’t like that, so I made my own list.

This is my ranking of genuinely high-protein breakfast cereals, which I define as containing at least 25% calories from protein.

Many food products like to advertise how many grams of protein they have per serving. That number doesn’t matter because it depends on how big a serving is. Hypothetically, if a food had 6g protein per serving but each serving contained 2000 calories, that would be a terrible deal. The actual number that matters is the proportion of calories from protein.

My ranking only includes vegan cereals because I’m vegan. Fortunately most cereals are vegan anyway. The main exception is that some cereals contain whey protein, but that’s not too common—most of them use soy, pea, or wheat protein instead.

High-protein cereals, ranked by flavor

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Conventional wisdom says charity cost-effectiveness obeys a power law. To my knowledge, this hypothesis has never been properly tested.¹ So I tested it and it turns out to be true.

(Maybe. Cost-effectiveness might also be log-normally distributed.)

• Cost-effectiveness estimates for global health interventions (from DCP3) fit a power law (a.k.a. Pareto distribution) with \(\alpha = 1.11\). [More]
• Simulations indicate that the true underlying distribution has a thinner tail than the empirically observed distribution. [More]

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Leftist critics of effective altruism like to say this. Well, it’s not true, and I proved it by calculating (an estimate of) the expected utility of a communist revolution. It wasn’t even hard—it took me less than an hour.

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I have some observations and half-baked ideas about my recent donation process. They weren’t important enough to include in the main post, but I want to talk about them anyway.

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Summary

Last updated 2024-11-20.

It’s been a while since I last put serious thought into where to donate. Well I’m putting thought into it this year and I’m changing my mind on some things.

I now put more priority on existential risk (especially AI risk), and less on animal welfare and global priorities research. I believe I previously gave too little consideration to x-risk for emotional reasons, and I’ve managed to reason myself out of those emotions.

Within x-risk:

• AI is the most important source of risk.
• There is a disturbingly high probability that alignment research won’t solve alignment by the time superintelligent AI arrives. Policy work seems more promising.
• Specifically, I am most optimistic about policy advocacy for government regulation to pause/slow down AI development.

In the rest of this post, I will explain:

1. Why I prioritize x-risk over animal-focused longtermist work and global priorities research.
2. Why I prioritize AI policy over AI alignment research.
3. My beliefs about what kinds of policy work are best.

Then I provide a list of organizations working on AI policy and my evaluation of each of them, and where I plan to donate.

Cross-posted to the Effective Altruism Forum.

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Psychologists have done experiments that supposedly show how people behave irrationally. But in some of those experiments, people do behave rationally, and it’s the psychologists’ expectations that are irrational.

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