Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: How To Get an EA-aligned Job: My Experience, published by sofiabalderson on December 3, 2021 on The Effective Altruism Forum.

In his speech at EAG London 2021 Benjamin Todd, the Founder and CEO of 80,000 hours, said that EA organisations tend to have talent bottlenecks in some roles, but that doesn’t mean that it’s necessarily easier to find a job. Unless you are very lucky, you’re likely to go through multiple application rounds and spend at least a couple of months before you get an offer you’re happy with. However, for some reason, the moment we get a job we forget just how hard it was to land one. When we decide to look for another role a couple of years later, it takes us by surprise. No wonder – job search is a skill that everyone should improve and keep up-to-date, as practices change every year.

I've recently been through a job search process which, admittedly, was a lot harder than I thought it would be. 3,5 years ago, I was super lucky to get my Veganuary job – it was the first and the only application I made as at that point I wasn’t planning on changing jobs. I naively thought that this time I’d too just walk straight into my next awesome role, but the reality was very different. It wasn’t only hard because I applied and got refused. I found myself having to reassess completely what I actually wanted from my next role and being lost in all the jobs that were being offered. I also wasn’t sure I actually wanted to leave my current job, which added extra complexity to the decision.

For your reference, I applied for about 10 jobs with targeted personalised applications and got responses, interviews and trial tasks for 5 of them. The whole process took about 5 months of relatively active search (with a couple of weeks off here and there) and I got my first and only offer in the fifth month - to be the Managing Director of Rethink Priorities’ first incubated charity, Insect Welfare Project (provisional name). I’m super pleased with this offer and can’t wait to get started in January. I think the resources and techniques I used contributed greatly to my getting it.

In this blog, I’ve written about all the resources I’ve used and any crucial information I’ve learnt while job searching. Partly in the hope that it will help someone, partly also for myself to remember what I went through. I’m going to be leading an organisation, so it would be great to keep in mind just how hard this is from a job applicant perspective. As it’s more like a library of steps and resources, it’s quite a long post - feel free to skip to the sections most relevant to you!

1. You need to figure out what kind of role you actually want to do

There is no point in applying for jobs if you don’t actually know what you want from a job. For example, I applied for a couple of first-level management position that fit my description really well. However, the reason I started thinking about a new job was because I wanted to be challenged and have more responsibility. If you fit 100% of the job description and literally doing the same or a slightly different job, you may find yourself being bored within 6 months.

Resources I recommend:

A Job To Love and How To Find a Job You'll Love to understand what kind work is best for you. I had done a lot of thinking in my life about careers before but these books still offered good tools and food for thought.

Squiggly Career book is about different ways to think about job progression and they also have a podcast.

80,000h career course– This is an 8-week self-paced course aimed at EA-aligned professionals. I really recommend going through this, you'll get an invite to a FB group where you'll be able to find a course buddy so that you can hold yourself accountable to finish and it's always good to get other people's input. At the end of the course, you'll have a...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Shulman and Yudkowsky on AI progress, published by Eliezer Yudkowsky on December 3, 2021 on LessWrong.

This post is a transcript of a discussion between Carl Shulman and Eliezer Yudkowsky, following up on a conversation with Paul Christiano and Ajeya Cotra.

Color key:

9.14. Carl Shulman's predictions

[Shulman][20:30]

I'll interject some points re the earlier discussion about how animal data relates to the 'AI scaling to AGI' thesis.

1. In humans it's claimed the IQ-job success correlation varies by job, For a scientist or doctor it might be 0.6+, for a low complexity job more like 0.4, or more like 0.2 for simple repetitive manual labor. That presumably goes down a lot with less in the way of hands, or focused on low density foods like baleen whales or grazers. If it's 0.1 for animals like orcas or elephants, or 0.05, then there's 4-10x less fitness return to smarts.

2. But they outmass humans by more than 4-10x. Elephants 40x, orca 60x+. Metabolically (20 watts divided by BMR of the animal) the gap is somewhat smaller though, because of metabolic scaling laws (energy scales with 3/4 or maybe 2/3 power, so ).

%27s_law

If dinosaurs were poikilotherms, that's a 10x difference in energy budget vs a mammal of the same size, although there is debate about their metabolism.

3. If we're looking for an innovation in birds and primates, there's some evidence of 'hardware' innovation rather than 'software.' Herculano-Houzel reports in The Human Advantage (summarizing much prior work neuron counting) different observational scaling laws for neuron number with brain mass for different animal lineages.

We were particularly interested in cellular scaling differences that might have arisen in primates. If the same rules relating numbers of neurons to brain size in rodents (6)

The brain of the capuchin monkey, for instance, weighing 52 g, contains >3× more neurons in the cerebral cortex and ≈2× more neurons in the cerebellum than the larger brain of the capybara, weighing 76 g.

[Editor’s Note: Quote source is “Cellular scaling rules for primate brains.”]

In rodents brain mass increases with neuron count n^1.6, whereas it's close to linear (n^1.1) in primates. For cortex neurons and cortex mass 1.7 and 1.0. In general birds and primates are outliers in neuron scaling with brain mass.

Note also that bigger brains with lower neuron density have longer communication times from one side of the brain to the other. So primates and birds can have faster clock speeds for integrated thought than a large elephant or whale with similar neuron count.

4. Elephants have brain mass ~2.5x human, and 3x neurons, but 98% of those are in the cerebellum (vs 80% in or less in most animals; these are generally the tiniest neurons and seem to do a bunch of fine motor control). Human cerebral cortex has 3x the neurons of the elephant cortex (which has twice the mass). The giant cerebellum seems like controlling the very complex trunk.

Blue whales get close to human neuron counts with much larger brains.

5. As Paul mentioned, human brain volume correlation with measures of cognitive function after correcting for measurement error on the cognitive side is in the vicinity of 0.3-0.4 (might go a bit higher after controlling for non-functional brain volume variation, lower from removing confounds). The genetic correlation with cognitive function in this study is 0.24:

So it accounts for a minority of genetic influences on cognitive ability. We'd also expect a bunch of genetic variance that's basically disruptive mutations in mutation-selection balance (e.g. schizophrenia seems to be a result of that, with schizophrenia alleles under negative selection, but a big mutational target, with the standing burden set by the level of fitness penalty for it; in niches with less return to cognition the mutational surface w...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Shulman and Yudkowsky on AI progress, published by Eliezer Yudkowsky on December 3, 2021 on LessWrong.

This post is a transcript of a discussion between Carl Shulman and Eliezer Yudkowsky, following up on a conversation with Paul Christiano and Ajeya Cotra.

Color key:

9.14. Carl Shulman's predictions

[Shulman][20:30]

I'll interject some points re the earlier discussion about how animal data relates to the 'AI scaling to AGI' thesis.

1. In humans it's claimed the IQ-job success correlation varies by job, For a scientist or doctor it might be 0.6+, for a low complexity job more like 0.4, or more like 0.2 for simple repetitive manual labor. That presumably goes down a lot with less in the way of hands, or focused on low density foods like baleen whales or grazers. If it's 0.1 for animals like orcas or elephants, or 0.05, then there's 4-10x less fitness return to smarts.

2. But they outmass humans by more than 4-10x. Elephants 40x, orca 60x+. Metabolically (20 watts divided by BMR of the animal) the gap is somewhat smaller though, because of metabolic scaling laws (energy scales with 3/4 or maybe 2/3 power, so ).

%27s_law

If dinosaurs were poikilotherms, that's a 10x difference in energy budget vs a mammal of the same size, although there is debate about their metabolism.

3. If we're looking for an innovation in birds and primates, there's some evidence of 'hardware' innovation rather than 'software.' Herculano-Houzel reports in The Human Advantage (summarizing much prior work neuron counting) different observational scaling laws for neuron number with brain mass for different animal lineages.

We were particularly interested in cellular scaling differences that might have arisen in primates. If the same rules relating numbers of neurons to brain size in rodents (6)

The brain of the capuchin monkey, for instance, weighing 52 g, contains >3× more neurons in the cerebral cortex and ≈2× more neurons in the cerebellum than the larger brain of the capybara, weighing 76 g.

[Editor’s Note: Quote source is “Cellular scaling rules for primate brains.”]

In rodents brain mass increases with neuron count n^1.6, whereas it's close to linear (n^1.1) in primates. For cortex neurons and cortex mass 1.7 and 1.0. In general birds and primates are outliers in neuron scaling with brain mass.

Note also that bigger brains with lower neuron density have longer communication times from one side of the brain to the other. So primates and birds can have faster clock speeds for integrated thought than a large elephant or whale with similar neuron count.

4. Elephants have brain mass ~2.5x human, and 3x neurons, but 98% of those are in the cerebellum (vs 80% in or less in most animals; these are generally the tiniest neurons and seem to do a bunch of fine motor control). Human cerebral cortex has 3x the neurons of the elephant cortex (which has twice the mass). The giant cerebellum seems like controlling the very complex trunk.

Blue whales get close to human neuron counts with much larger brains.

5. As Paul mentioned, human brain volume correlation with measures of cognitive function after correcting for measurement error on the cognitive side is in the vicinity of 0.3-0.4 (might go a bit higher after controlling for non-functional brain volume variation, lower from removing confounds). The genetic correlation with cognitive function in this study is 0.24:

So it accounts for a minority of genetic influences on cognitive ability. We'd also expect a bunch of genetic variance that's basically disruptive mutations in mutation-selection balance (e.g. schizophrenia seems to be a result of that, with schizophrenia alleles under negative selection, but a big mutational target, with the standing burden set by the level of fitness penalty for it; in niches with less return to cognition the mutational surface w...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Covid 12/2: But Aside From That, published by Zvi on December 2, 2021 on LessWrong.

This weekly post is for things that are not Omicron. For the Omicron news, see my Omicron updates (#1, #2, #3), the last of which came out the same day as this post. There’s an extensive speed premium, so I’ve gone to a more-than-weekly schedule on that, although I hope to return to weekly-only soon and only do 0-2 more non-Thursday Omicron posts. We’ll see how that goes.

The news other than Omicron was relatively quiet and expected, except for the part where we are at best barely approving Molnupiravir (post on that here). Too quiet, as case counts and deaths dropped an extreme amount due to the holiday, far more than I expected. That makes predicting next week’s numbers especially hard, but I expect a lot of cases and deaths to be officially tallied next week.

Executive Summary

Omicron is coming, see update posts (#1, #2, #3).

Paxlovid remains illegal, Molnupiravir is touch and go.

Cases continue dropped a lot and so did deaths, presumably due to Thanksgiving.

Let’s run the numbers.

The Numbers

Predictions

Prediction from last week: 620k cases (+3%) and 7,240 deaths (-5%).

Results: 528k cases (-11%) and 6,329 deaths (-16%).

Prediction for next week: 630k cases (+20%) and 8,000 deaths (+26%)

I did attempt to adjust for the holiday, I swear I did, but this was a gigantic swing in reporting, much much bigger than last year’s. We know it is reporting because it shows up in both cases and deaths, and there’s no possible way deaths fell substantially last week. That means that there’s a shortfall of at least a thousand ‘missing’ deaths that haven’t been reported yet, and at least 100k cases.

There’s always some chance a miracle happened given how large the change was, but that’s not what I expect.

How much of that gets missed permanently, versus how much gets put into next week, is unclear, so this is one of the hardest weeks to predict.

Deaths

Cases

Vaccinations

It doesn’t look like all the noise about Omicron is motivating people to get boosted or vaccinated, and it’s now been a while since kids were eligible. Numbers declined a lot.

It’s not the WHO this time, it’s the WTO, and I’m not generally a fan of waving patent rights, if you want the patent rights then there’s a price at which they are for sale and you can always go ahead and Cut Lex Luther a Check, but wow should the you not let a new variant stop you? I’ll let Zeynep take this one.

I do think you want to buy or otherwise urgently arrange for patent/IP waivers for the antivirals, that’s probably the most important thing right now, but seriously, there’s no big fundamental skills/secrets issue like there is with mRNA, this isn’t hard, at most all you have to do is cut the check.

Permanent Midnight

There’s saying the quiet part out loud. And then there’s not realizing it was even supposed to be the quiet part in the first place.

There it is:

“We’re never going to go back to normal. Personally, I don’t think I will ever get on a plane without wearing a mask,” said Patti Solis Doyle, a Democratic strategist who worked closely with Biden during Barack Obama’s 2008 presidential campaign.

Meanwhile, the continued delusion that somehow credit is deserved for the existence of human life, or something.

“The administration has gotten us to a place where we can do things, where we can see our family and our friends and go shopping and go to a movie and do the things that bring back normalcy into our lives,” Solis Doyle said. “I don’t think he gets enough credit for that.”

It’s up to you whether to do those things. The only role ‘the administration’ has in that question is whether they’re going to use men with guns to stop you.

As noted above, he’s tying that decision to vaccination rates rather than tying it to any actual cost-benefit cal...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Covid 12/2: But Aside From That, published by Zvi on December 2, 2021 on LessWrong.

This weekly post is for things that are not Omicron. For the Omicron news, see my Omicron updates (#1, #2, #3), the last of which came out the same day as this post. There’s an extensive speed premium, so I’ve gone to a more-than-weekly schedule on that, although I hope to return to weekly-only soon and only do 0-2 more non-Thursday Omicron posts. We’ll see how that goes.

The news other than Omicron was relatively quiet and expected, except for the part where we are at best barely approving Molnupiravir (post on that here). Too quiet, as case counts and deaths dropped an extreme amount due to the holiday, far more than I expected. That makes predicting next week’s numbers especially hard, but I expect a lot of cases and deaths to be officially tallied next week.

Executive Summary

Omicron is coming, see update posts (#1, #2, #3).

Paxlovid remains illegal, Molnupiravir is touch and go.

Cases continue dropped a lot and so did deaths, presumably due to Thanksgiving.

Let’s run the numbers.

The Numbers

Predictions

Prediction from last week: 620k cases (+3%) and 7,240 deaths (-5%).

Results: 528k cases (-11%) and 6,329 deaths (-16%).

Prediction for next week: 630k cases (+20%) and 8,000 deaths (+26%)

I did attempt to adjust for the holiday, I swear I did, but this was a gigantic swing in reporting, much much bigger than last year’s. We know it is reporting because it shows up in both cases and deaths, and there’s no possible way deaths fell substantially last week. That means that there’s a shortfall of at least a thousand ‘missing’ deaths that haven’t been reported yet, and at least 100k cases.

There’s always some chance a miracle happened given how large the change was, but that’s not what I expect.

How much of that gets missed permanently, versus how much gets put into next week, is unclear, so this is one of the hardest weeks to predict.

Deaths

Cases

Vaccinations

It doesn’t look like all the noise about Omicron is motivating people to get boosted or vaccinated, and it’s now been a while since kids were eligible. Numbers declined a lot.

It’s not the WHO this time, it’s the WTO, and I’m not generally a fan of waving patent rights, if you want the patent rights then there’s a price at which they are for sale and you can always go ahead and Cut Lex Luther a Check, but wow should the you not let a new variant stop you? I’ll let Zeynep take this one.

I do think you want to buy or otherwise urgently arrange for patent/IP waivers for the antivirals, that’s probably the most important thing right now, but seriously, there’s no big fundamental skills/secrets issue like there is with mRNA, this isn’t hard, at most all you have to do is cut the check.

Permanent Midnight

There’s saying the quiet part out loud. And then there’s not realizing it was even supposed to be the quiet part in the first place.

There it is:

“We’re never going to go back to normal. Personally, I don’t think I will ever get on a plane without wearing a mask,” said Patti Solis Doyle, a Democratic strategist who worked closely with Biden during Barack Obama’s 2008 presidential campaign.

Meanwhile, the continued delusion that somehow credit is deserved for the existence of human life, or something.

“The administration has gotten us to a place where we can do things, where we can see our family and our friends and go shopping and go to a movie and do the things that bring back normalcy into our lives,” Solis Doyle said. “I don’t think he gets enough credit for that.”

It’s up to you whether to do those things. The only role ‘the administration’ has in that question is whether they’re going to use men with guns to stop you.

As noted above, he’s tying that decision to vaccination rates rather than tying it to any actual cost-benefit cal...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: $100/$50 rewards for good references, published by Stuart Armstrong on December 3, 2021 on The AI Alignment Forum.

Dear LessWrongers, this is an opportunity to make money and help with AI alignment.

We're looking for specific AI capabilities; has anyone published on the following subject:

Generating multiple reward functions or policies from the same set of challenges. Has there been designs for deep learning or similar, in which the agent produces multiple independent reward functions (or policies) to explain the same reward function or behaviour?

For example, in CoinRun, the agent must get to the end of the level, on the right, to collect the coin. It only gets the reward for collecting the coin.

That is the "true" reward, but, since the coin is all the way to the right, as far as the agent knows, "go to the far right of the level" could just as well have been the true reward.

We'd want some design that generated both these reward functions (and, in general, generated multiple reward functions when there are several independent candidates). Alternatively, they might generate two independent policies - we could test these by putting the coin in the middle of the level and seeing what the agent decided to do.

We're not interested in a Bayesian approach that lists a bunch of reward functions and then updates to include just those two (that's trivially easy to do). Nor are we interested in an IRL-style approach that lists "features", including the coin and the right hand side.

What we'd want is some neural-net style design that generates the coin reward and the move-right reward just from the game data, without any previous knowledge of the setting.

So, does anyone know any references for that kind of work?

We will pay $50 for the first relevant reference submitted, and $100 for the best reference.

Thanks!

Thanks for listening. To help us out with The Nonlinear Library or to learn more, please visit nonlinear.org.

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: $100/$50 rewards for good references, published by Stuart Armstrong on December 3, 2021 on The AI Alignment Forum.

Dear LessWrongers, this is an opportunity to make money and help with AI alignment.

We're looking for specific AI capabilities; has anyone published on the following subject:

Generating multiple reward functions or policies from the same set of challenges. Has there been designs for deep learning or similar, in which the agent produces multiple independent reward functions (or policies) to explain the same reward function or behaviour?

For example, in CoinRun, the agent must get to the end of the level, on the right, to collect the coin. It only gets the reward for collecting the coin.

That is the "true" reward, but, since the coin is all the way to the right, as far as the agent knows, "go to the far right of the level" could just as well have been the true reward.

We'd want some design that generated both these reward functions (and, in general, generated multiple reward functions when there are several independent candidates). Alternatively, they might generate two independent policies - we could test these by putting the coin in the middle of the level and seeing what the agent decided to do.

We're not interested in a Bayesian approach that lists a bunch of reward functions and then updates to include just those two (that's trivially easy to do). Nor are we interested in an IRL-style approach that lists "features", including the coin and the right hand side.

What we'd want is some neural-net style design that generates the coin reward and the move-right reward just from the game data, without any previous knowledge of the setting.

So, does anyone know any references for that kind of work?

We will pay $50 for the first relevant reference submitted, and $100 for the best reference.

Thanks!

Thanks for listening. To help us out with The Nonlinear Library or to learn more, please visit nonlinear.org.

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: EA megaprojects continued, published by mariushobbhahn on December 3, 2021 on The Effective Altruism Forum.

This post is a continuation/extension of the EA megaproject article by Nathan Young. Our list is by no means exhaustive, and everyone is welcome to extend it in the comments.

The people who contributed to this article are in no particular order: Simon Grimm, Marius Hobbhahn, Max Räuker, Yannick Mühlhäuser and Jasper Götting.

We would like to thank Aaron Gertler for his feedback and suggestions.

Donating to GiveDirectly is often seen as a scalable baseline for the effectiveness of a project. Another scalable baseline could be investing in clean energy research. We don’t expect all of the following ideas to meet either bar. Some of them might just be interesting or thought-provoking. The value of these megaprojects depends, among other things, on your risk profile, your views on patient philanthropy and the option value of the idea.

Movement building:

There are many EA organizations in the larger space of “getting more EAs into the right position”, such as 80K, different scholarships, local chapters, etc. However, given that EA is currently more people- than resource-constrained, it seems plausible that there is much room for growth.

Some people have argued to “keep EA small” (see here or here). However, we don’t think it makes sense to limit access to the movement to the extent that EA would stop growing. A plausible future role for EA in society might be similar to science. Everyone broadly knows what science is, and most people have a good opinion of it, but most people aren’t scientists themselves. Most scientific fields are competitive, select for highly talented people, and grow somewhere between linearly and exponentially. The goal should not be to approach as many people as possible (such as religions) but to approach and funnel talented individuals into the movement (such as sports organizations or universities).

Ideas for bigger projects in the space of movement building include:

1. A large global scholarship foundation:

EAs could build a foundation that helps young people with their choice of study while providing financial support, visas and mentorship during their studies. Such a foundation would also yield an extensive, global network of young, talented individuals. Furthermore, such a foundation can scout for talented individuals earlier on through similar events like existing math/science/coding/forecasting tournaments or recruiting in sports. Some of these elements already exist on a smaller and more informal scale, but a global institution would provide increased visibility, prestige, and scaling effects. Furthermore, a scholarship foundation could focus specifically on developing countries and identify talents who could migrate to richer countries. The aim would be to create an organization that lends as much prestige as Fulbright or Rhodes scholarships.

2. Buy a Big Newspaper:

Philanthropists and other wealthy individuals regularly buy or own major news outlets (Jeff Bezos bought WaPo, Rupert Murdoch owns vast swathes of center-right to right-wing media). An affluent EA could buy a trusted newspaper and direct it in a more evidence-based direction, for instance, by incorporating forecasts into reporting or by highlighting certain positive developments that get neglected. Future Perfect has shown how EA-influenced reporting can have a place in a major news outlet, and the Ezra Klein show sometimes uses an EA-framing, but we think there is room for more. This could have a particularly relevant impact in non-English speaking countries.

Science:

1. Fund very large RCTs/population-wide studies

Conventional RCTs are often too underpowered for certain (often population-wide) relations. Nonetheless, knowing of these relationships can be impactful, as ...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: EA megaprojects continued, published by mariushobbhahn on December 3, 2021 on The Effective Altruism Forum.

This post is a continuation/extension of the EA megaproject article by Nathan Young. Our list is by no means exhaustive, and everyone is welcome to extend it in the comments.

The people who contributed to this article are in no particular order: Simon Grimm, Marius Hobbhahn, Max Räuker, Yannick Mühlhäuser and Jasper Götting.

We would like to thank Aaron Gertler for his feedback and suggestions.

Donating to GiveDirectly is often seen as a scalable baseline for the effectiveness of a project. Another scalable baseline could be investing in clean energy research. We don’t expect all of the following ideas to meet either bar. Some of them might just be interesting or thought-provoking. The value of these megaprojects depends, among other things, on your risk profile, your views on patient philanthropy and the option value of the idea.

Movement building:

There are many EA organizations in the larger space of “getting more EAs into the right position”, such as 80K, different scholarships, local chapters, etc. However, given that EA is currently more people- than resource-constrained, it seems plausible that there is much room for growth.

Some people have argued to “keep EA small” (see here or here). However, we don’t think it makes sense to limit access to the movement to the extent that EA would stop growing. A plausible future role for EA in society might be similar to science. Everyone broadly knows what science is, and most people have a good opinion of it, but most people aren’t scientists themselves. Most scientific fields are competitive, select for highly talented people, and grow somewhere between linearly and exponentially. The goal should not be to approach as many people as possible (such as religions) but to approach and funnel talented individuals into the movement (such as sports organizations or universities).

Ideas for bigger projects in the space of movement building include:

1. A large global scholarship foundation:

EAs could build a foundation that helps young people with their choice of study while providing financial support, visas and mentorship during their studies. Such a foundation would also yield an extensive, global network of young, talented individuals. Furthermore, such a foundation can scout for talented individuals earlier on through similar events like existing math/science/coding/forecasting tournaments or recruiting in sports. Some of these elements already exist on a smaller and more informal scale, but a global institution would provide increased visibility, prestige, and scaling effects. Furthermore, a scholarship foundation could focus specifically on developing countries and identify talents who could migrate to richer countries. The aim would be to create an organization that lends as much prestige as Fulbright or Rhodes scholarships.

2. Buy a Big Newspaper:

Philanthropists and other wealthy individuals regularly buy or own major news outlets (Jeff Bezos bought WaPo, Rupert Murdoch owns vast swathes of center-right to right-wing media). An affluent EA could buy a trusted newspaper and direct it in a more evidence-based direction, for instance, by incorporating forecasts into reporting or by highlighting certain positive developments that get neglected. Future Perfect has shown how EA-influenced reporting can have a place in a major news outlet, and the Ezra Klein show sometimes uses an EA-framing, but we think there is room for more. This could have a particularly relevant impact in non-English speaking countries.

Science:

1. Fund very large RCTs/population-wide studies

Conventional RCTs are often too underpowered for certain (often population-wide) relations. Nonetheless, knowing of these relationships can be impactful, as ...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Has anything in the EA global health sphere changed since the critiques of "randomista development" 1-2 years ago?, published by MaxG on December 2, 2021 on The Effective Altruism Forum.

Just recently I re-discovered posts from 1-2 years ago which discussed critiques of the GiveWell style approach to global health, namely the focus on RCTs.

For example, Ben Kuhn's article suggested that maybe we should look more at the cost-effectiveness of political change. Then there is the wildly popular post on growth and randomista development, which suggested that maybe we should pay more attention to economic growth interventions.

I am sure these articles sparked lots of discussion in the GiveWell and EA global health community. My question is: Has anything changed since then? Has anyone put more resources into researching these topics? How has the thinking of EA global health people developed?

Thanks for listening. To help us out with The Nonlinear Library or to learn more, please visit nonlinear.org.