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: What are sane reasons that Covid data is treated as reliable?, published by Valentine on January 1, 2022 on LessWrong.

I feel like this is one of those questions that's somehow too basic to ask. Or maybe too political. Like wondering this stuff implies I'm siding with something.

I'd really like to just set all the politics aside, simply name my ignorance, and hear some truthful answers. Because seeing this not even discussed is part of what's giving me a sense of "Fuck it, no one actually knows anything, everyone is just making random shit up that fits their social identities."

People keep talking confidently about incidence rates for different Covid variants, about their death rates and likelihood of hospitalization, how we have such clear data on vaccine efficacy and safety, etc.

But all the info streams I see look extremely dubious. I don't just mean one could doubt them. I mean, I've witnessed powerful evidence of blatant bias, and aside from brief mentions of the existence of those biases no one seems to care.

I'll give some examples below that inspire my confusion. I want to emphasize that I'm honestly asking here. Less Wrong is one of the few places (the only place?) where I feel like I can seek epistemic cleaning and clarity here.

So, example confusion sources:

I've personally known many people who have had serious medical problems that sure looked clearly like vaccine reactions. On par with "Well now I can't get out of bed and can't think anymore" or "Oh shit, heart attack" kinds of reactions. But all these people I've known who tried to report their reactions were told "No, your reaction can't have been due to the vaccine, because the vaccine is safe and effective." I've heard lots of similar reports. Because this is about rejecting data collection, I don't see how anyone could possibly know how common/rare this is.

I've never known anyone who has been tested for a variant of any kind. I don't know anything about how variant tests look different from a generic Covid test. So where are these numbers for variant spread coming from? Maybe hospitals do have special genetic tests and reliably do those? But then isn't there going to be a pretty strong bias based on the fact that these are only for people who are getting hospitalized?

There was, best as I could tell, active (if unintentional) data destruction quite early on in the pandemic. Maybe it's still going on? Hospitals had financial incentives to find reasons why people who died had died of Covid. Lots of bodies got cremated before autopsies could happen. So how strong was that bias? I never heard any curiosity about the implications of this for our sense of how deadly the virus actually was. (Plenty of hand-wringing and finger-pointing though.)

I get that we can deduce something about the virus's spread based on (a) how many hospitalizations for Covid different areas are getting plus (b) some assumptions about the exponential-flavored spread of the infections. That lets us use math to peer into how many non-hospitalized infection cases there must be, and roughly how long the incubation-to-infectious timing should be.

But how in the bajeezus can anyone possibly extrapolate from there to how long it takes to become symptomatic? In need of hospitalization, sure, but how is anyone getting not-heavily-biased data on symptom strength below the hospitalization threshold?

I don't care about symptomatic-ness per se. This is just one of a ton of examples about info passing through the info commons that people — including here! — seem bizarrely (to me) super confident in despite the devastation and weaponization of said info commons.

I could believe there's some solid clear reasoning going on in the background here, grounded in hard-to-refute data, that makes all these assertions about Covid's variants and death rat...

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: Hits-based development: funding developing-country economists, published by Michael Wiebe on January 1, 2022 on The Effective Altruism Forum.

So far, the effective altruist strategy for global poverty has followed a high-certainty, low-reward approach. GiveWell mostly looks at charities with a strong evidence base, such as bednets and cash transfers. But there's also a low-certainty, high-reward approach: promote catch-up economic growth. Poverty is strongly correlated with economic development (urbanization, industrialization, etc), so encouraging development would have large effects on poverty. Whereas cash transfers have a large probability of a small effect, promoting growth has a small probability of a large effect. (In general, we should diversify across high- and low-risk strategies.) In short, can we do “hits-based development”?[1]

How can we affect growth? Tractability is the main problem for hits-based development, since GDP growth rates are notoriously difficult to change. However, there are a few promising options. One specific mechanism is to train developing-country economists, who then work in government and influence policy in a pro-growth direction, ultimately increasing the probability of a growth episode. The key is that local experts have local knowledge of culture, politics, and law, which allows them to understand the impediments to growth in a way that foreign World Bank consultants cannot.[2] The goal is not to find the specific interventions that will boost growth in a specific country, but rather to find the experts who will be most capable of finding those interventions.

For example, Lant Pritchett mentions a think tank in India that influenced its liberalizing reforms, which preceded a large growth episode.[3] One way to think of the mechanism is having an economist "in the room" at a key moment to prevent the president from enacting a policy that would lead to hyperinflation (say). Since such an action is highly consequential, but also unlikely, it plausibly has high expected value.[4] A different angle is that local experts will improve the quality of solutions to important problems (urbanization, industrialization, health care, etc). This translates into a concrete goal: increase the number of local experts in developing countries, to increase the chance of a growth episode.

In terms of crowdedness, there are, for example, very few Africans doing economics PhDs in the US. Some developing countries (like China) already have many economists, and do not need to be targeted. One feature of this proposal is that, in principle, it has a clear stopping point: train developing-country economists until you reach N per country.

There are many ways that extra funding could relax budget constraints: GRE fees; PhD application fees; scholarships for undergrads, masters, and PhD students; research projects; thesis prizes; subsidizing RAships; TA buyouts; conference fees and travel; think tanks; translating textbooks; bootcamps/workshops/conferences; sabbaticals/research visits; and so on.

From initial conversations, it seems that the key constraint is the number of well-trained students who can get accepted into top PhD programs. Hence, funding should be targeted to training undergrads and Master's students, as the African School of Economics is doing.[5] Hence, one option is to fund ASE and its satellite campuses. Donations need not be restricted to scholarships, for two reasons. First, because of the fungibility problem, such restrictions are difficult to guarantee. Second, ASE's values are consistent with EA values. This matches GiveWell's advice to "find an organization whose existing priorities you are comfortable with – and give unrestricted."

Footnotes:

[1] See recent discussions on the forum here and here.[2] Wantchekon says that his knowledge...

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: $1000 USD prize - Circular Dependency of Counterfactuals, published by Chris Leong on January 1, 2022 on The AI Alignment Forum.

I've previously argued that the concept of counterfactuals can only be understood from within the counterfactual perspective.

I will be awarding a $1000 prize for the best post that engages with the idea that counterfactuals may be circular in this sense. The winning entry may be one of the following (these categories aren't intended to be exclusive):

a) A post that attempts to draw out the consequences of this principle for decision theory

b) A post that attempts to evaluate the arguments for and against adopting the principle that counterfactuals only make sense from within the counterfactual perspective

c) A review of relevant literature in philosophy or decision theory

d) A post that states already existing ideas in a clearer manner (I don't think this topic has been explored much on LW, but it may have in explored in the literature on decision theory or philosophy)

Feel free to ask me for clarification about what would be on or off-topic. Probably the main thing I'd like to see is substantial engagement with this principle.If someone submits a high-quality post that only touches on this issue tangentially, but someone else submits an only okayish post that tries to deeply engage with this issue, then I would likely award it to the latter as I'm trying to incentivise more engagement with this issue rather than just high-quality posts generally. If the bounty is awarded to an unexpected submission, I expect this to be the main cause.

I will be awarding an additional $100 for the best short-form post on this topic. This may be a LW Shortform post, a public Facebook post, a Twitter thread, ect (I'm not going to include Discord/Slack messages as they aren't accessible).

Why do I believe in this principle?Roughly, my reasons are as follows:

Rejecting David Lewis' Counterfactual Realism as absurd and therefore concluding that counterfactuals must be at least partially a human construction: either a) in the sense of them being an inevitable and essential part of how we make sense of the world by our very nature or b) in the sense of being a semi-arbitrary and contingent system that we've adopted in order to navigate the world

Insofar as counterfactuals are inherently a part of how we interpret the world, the only way that we can understand them is to "look out through them", notice what we see, and attempt to characterise this as precisely as possible

Insofar as counterfactuals are a somewhat arbitrary and contingent system constructed in order to navigate the world, the way that the system is justified is by imagining adopting various mental frameworks and noticing that a particular framework seems like it would be useful over a wide variety of circumstances. However, we've just invoked counterfactuals twice: a) by imagining adopting different mental frameworks b) by imagining different circumstances over which to evaluate these frameworks. ().

In either case, we seem to be unable to characterise counterfactuals without depending on already having the concept of counterfactuals. Or at least, I find this argument persuasive.

Why do I believe this is important?

I've argued for the importance of agent meta-foundations before. Roughly, there seems to be a lot of confusion about what counterfactuals are and how to construct them. I believe that much of this confusion would be cleared up if we can sort out some of these foundational issues. And the claim that counterfactuals can only be understood from an interior perspective is one such issue.

Why am I posting this bounty?

I believe in this idea, but:

I haven't been able to dedicate nearly as much to time exploring this as I would like in between all of my other commitments

Working on this approac...

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: This Year I Tried To Teach Myself Math. How Did It Go?, published by Borasko on December 31, 2021 on LessWrong.

Cross-posted from my substack

Summary:

This goes over a year of self teaching, the topics of math I was able to learn, how I view the best way to learn math and what my general view of math as a whole is with my limited current knowledge on the subject. At the end is a guide I made for anybody else that wishes to teach themselves math, no matter the skill level.

The Wild World of Online Learning:

When you first start out to do any project it's usually best to have instructions and a goal of what a ‘finished’ state looks like in mind. Most consumer bought appliances that require assembly come with a very hand holdy instruction sheet and all the parts required, the same can not be said for trying to learn an area of knowledge. I would argue a good chuck, >60% of the year, was just spent on figuring out what I needed to learn and where the next logical step from that was.

I will fully admit that GPT-3 and other AI advances got me interested in joining the industry. I will also fully admit that I immediately jumped into Coursera’s Intro to Machine Learning and crashed hard when I realized I wasn't getting anything out of it because I barely knew high school algebra and couldn't understand what I was looking at, because I wasn't interested in anything math related until now. Even reading about what partial derivatives were, there was too much scaffolding of previous calculus math needed to really set me up for understanding. The kicker was I could still pass the coding assignments and it made my “grade” look good, but that’s mostly because of how the course was set up. I complained about online learning because of it here, but I’m going to be a bit more dispassionate in this review.

This cycle repeated in a few more online courses. I would spend time going through the course, get by doing the assignments, start to get frustrated because I didn’t know why what I was learning was going to be important down the road, then eventually drop the course and start looking for another one that I felt could actually help me. It was about half way through the year that I decided to drop online learning due to the resentment of feeling like I wasted my time on the courses. The problem was I could do all the practice problems of the online courses fine, but I still felt like I had only islands of unconnected knowledge and I hadn't learned anything actually useful. It was like college all over again, just doing enough to get a grade then moving on. Many courses seem designed to give a myopic view of a topic, a few practice problems, and then moving onto another topic.

I’m more okay with colleges doing it since you get a degree and usually classes build off each other, but knowing I wouldn't get any recognition and most courses dead stopped after it felt bitter. But I do have my share of the blame of course.

I didn’t know what to look for, and so what I found probably wasn't that good. I won’t say perfect learning materials exist, or that most online learning courses are not just a trade of money for a sense of accomplishment. But knowing what I was supposed to learn and would have helped me beforehand would have helped me filter courses much better. So what happens is what I’m calling the autodidact's paradox: you need to already have the knowledge about what you want to learn about to get the best learning resources for that subject. This is of course easily solved by mentors or professors giving guidance in the field, but I did this on my own and was struggling.

Even though I wouldn't rate my experience with online courses very high, it wasn't pointless to me because it helped me learn what I wanted to avoid in an online course. And the insular knowledge tidbits sometimes di...

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: Prediction Markets in The Corporate Setting, published by NunoSempere on December 31, 2021 on The Effective Altruism Forum.

What follows is a report that Misha Yagudin, Nuño Sempere, and Eli Lifland wrote back in October 2021 for Upstart, an AI lending platform that was interesting in exploring forecasting methods in general and prediction markets in particular.

We believe that the report is of interest to EA as it relates to the institutional decision-making cause area and because it might inform EA organizations about which forecasting methods, if any, to use. In addition, the report covers a large number of connected facts about prediction markets and forecasting systems which might be of interest to people interested in the topic.

Note that since this report was written, Google has started a new internal prediction market. Note also that this report mostly concerns company-internal prediction markets, rather than external prediction markets or forecasting platforms, such as Hypermind or Metaculus. However, one might think that the concerns we raise still apply to these.

Executive Summary

We reviewed the academic consensus on and corporate track record of prediction markets.

We are much more sure about the fact that prediction markets fail to gain adoption than about any particular explanation of why this is.

The academic consensus seems to overstate their benefits and promisingness. Lack of good tech, the difficulty of writing good and informative questions, and social disruptiveness are likely to be among the reasons contributing to their failure.

We don't recommend adopting company-internal prediction markets for these reasons. We see room for exceptions: using them in limited contexts or delegating external macroeconomic questions to them.

We survey some alternatives to prediction markets. Generally, we prefer these alternatives' pros and cons.

Introduction

This section:

Defines prediction markets

Outlines their value proposition

What are prediction markets

Prediction markets are markets in which contracts are traded that have some value if an event happens, and no value if an event doesn't happen. For example, a share of "Democrat" in a prediction market on the winner of the 2024 US presidential election will pay $1 if the winner of the 2024 election is a Democrat, and $0 if the winner is not.

Prices in prediction markets can be interpreted as probabilities. For example, the expected value of a "Democrat" contract in the previous market is $1⋅p+$0⋅(1−p), where p is the chance that a Democrat will win. To the extent that the market is efficient, one expects the expected value of a contract to be equal to its current value. So if one observes a contract price of $0.54, one can deduce the expected probability by setting $0.54=$1∗p+$0∗(1−p), and thus p=0.54=54%. It is also in this sense that one says that "the market as a whole expects" Democrats to win with 54% probability.

One might expect markets not to be efficient, for instance after remembering Keynes' adage that "markets can remain irrational longer than you can remain solvent." And we do see inefficiencies in modern prediction markets, sometimes glaring. However, note that, unlike the stock market, prediction markets have hard deadlines after which the market comes in contact with reality and gets resolved.

Besides binary prediction markets, there are also markets with multiple options—e.g., "Who will win the 2024 election?", with multiple contracts only one of which will pay out in the end,— or markets that pay out proportionally to some yet unknown number—e.g., "How many Senate seats will Republicans control after the 2022 elections?", which pays out proportionally to the number of seats.

Predictions markets thrive in some niches:

Bookmakers are offering odds and taking bets for notable sports events.

M...

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: Omicron Post #11, published by Zvi on December 31, 2021 on LessWrong.

The traditional action for New Year’s Eve is to go to a party, stay up until midnight and watch the bell drop, and the traditional song is Auld Lang Syne.

I’ve stopped doing any of these things.

My traditional action is now to watch a bunch of great college football games that are the cumulation of the year’s season, and maybe have a few friends over.

As for songs, I have two. First, to look back on the year and the ways one is stuck, and think about and motivate what one might change, Anastasia’s Same Old Story. Then, to remind that life is awesome and my life is awesome, Jewel’s This Way.

It’s important to have rituals and traditions that speak to you.

It’s also important to contain multitudes.

The darkness is coming. So is the light.

Life is good. All signs point towards the golden scenario of a quick peak under reduced severity.

Happy new year!

Around the World

South Africa, the UK and Denmark are no longer uniquely informative places, so I’m combining all the areas into one section now.

There are a few cities where we may have peaked, but otherwise outside of South Africa? Not so much.

Cases still growing in the UK, but perhaps no longer growing in London.

Here’s a UK hospital update, from the comments via MoveMyCat:

I work at a large teaching hospital in the UK, but outside London. I’ve been isolating for a while (very mild if any symptoms, but was undergoing regular testing at work). Prior to my isolation, there were rising numbers, but they were still using respiratory beds for ‘Incidental Covid’ types. Anecdotal observation supported the idea that staff isolation and the need to isolate from vulnerable Covid-negatives was more of a challenge at that point than any influx of very unwell Covid pneumonitis types.

I’ll aim to update when I’m next in work.

I’m back in work now. There’s a small uptick in actual respiratory admissions but most of the increased Covid capacity the hospital has put together is being filled by geriatrics patients who happen to have Covid. A friend who works at a nearby hospital reports that she has about a quarter of her unit in self isolation for one reason or other.

It’s actually remarkable how few people are coming in with fevers and breathlessness.

If we do have access to the numbers on any of that, please do share, for now I only know of such numbers in San Francisco.

Germany is behind other countries in terms of spread and has stalled for time, but it won’t be that much time, and it will change very little. That last week in gray isn’t fully reported yet, so this is the same old story as everywhere else.

In hindsight, that first Boston wastewater jump was probably Delta. The one now, not so much:

If we use the black line, the average is now over four times the previous maximum level. If we use the recent data points as an indication, there’s at least one more doubling after that.

An update from San Francisco, where the ‘with Covid’ and ‘for Covid’ distinction is actually being measured (and I’m happy that Dr. Fauci has ‘broken the seal’ and is talking about the distinction in earnest.)

This seems roughly right to me. If you have a highly vaccinated population and solid hospital capacity, even with this rapid a storm, we should mostly be able to muddle through, things can only add to 100% (and presumably cap out at more like a total of 80%-90%). If the peak is as high in rural areas with less beds and less vaccinations, I’d be a lot more nervous.

Note that San Francisco’s positive rate is somewhat lower than the national average, so it’s probably missing a slightly lower percentage of its cases than other places.

21% of NYPD is out sick.

21% of the NYPD was out sick Thursday in what outgoing Police Commissioner Dermot Shea said was the highest level of sick empl...

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: Counterexamples to some ELK proposals, published by Paul Christiano on December 31, 2021 on The AI Alignment Forum.

In this post I’ll describe some possible approaches to eliciting latent knowledge (ELK) not discussed in our report. These are basically restatements of proposals by Davidad, Rohin, Ramana, and John Maxwell. For each approach, I’ll present one or two counterexamples that I think would break it.

I think of these approaches as being based on two fundamental hopes:

If our AI tampers with one sensor, there may be other sensors we could deploy, or experiments we could run, in order to notice that something fishy has happened. We could have an AI help us design those experiments/sensors.

Actions that covertly tamper may be similar to actions that visibly tamper, e.g. they may exploit similar unknown facts about physics or they may involve common substeps. So we might be able to notice covert tampering by pointing out similar action sequences that lead to visible tampering.

While these hopes may hold in practice, in this post I’ll describe possible situations where they would fail. (I don't think anyone particularly expected these approaches to be counterexample-proof.)

I’m only claiming that these failures are possible. I think it is also interesting to try to generate the most probable failure cases and to actually estimate their probability, but that’s definitely not what I’m doing here.

Strategy: Hold out sensors

(Proposal #1 here)

In addition to asking my model to predict one set of sensors, I can deploy a second “held out” set (e.g. more cameras or seismographs or microphones.). I don’t ask my model to predict those sensors, but I show them to a human before getting them to answer questions. Hopefully that sometimes lets them notice tampering that would have been invisible without the new sensors.

Worry: human simulator can use all the extra sensor readings too

I’m concerned that I will learn a better version of the human simulator which predicts the readings of all sensors and then outputs what a human would infer from the complete set. That task need not be much more computationally demanding than the original human simulator.

Moreover, my AI may be able to deduce the presence of the additional sensors very cheaply. Perhaps it can notice the sensors, or it can learn about my past actions to get a hint about where I may have placed them. If this is possible, then “Predict the readings of all sensors” need not be much more complex than “Predict the readings of one sensor.”

An AI doesn’t have to be exactly correct about what sensors exist—when in doubt it can just predict what the sensor would say and imagine giving it to the human.

Strategy: Predict hypothetical sensors

(Proposal #2 here, also suggested with counterexample by Rohin in private communication)

Instead of installing a single sensor, I could randomly choose what sensors to deploy and how to install them. Then I could train a model Predict+ to map (description of sensor) → (sensor reading), and I could give a human the readings from many hypothetical sensors in order to give them the best chance of detecting tampering.

Worry 1: Predict+ may not be reasonable on non-existent sensors

To collect training data for Predict+, I actually install sensors and then use their readings as a target. In every situation, my AI is being asked to predict the readings from a sensor that I really did build.

If my AI is able to deduce the presence of sensors (as described in the last counterexample), then it’s unclear how Predict+(sensor description) will behave when we give it a description of a sensor that does not in fact exist.

In particular, Predict+(sensor description) might behave in the following undesirable way: it predicts the future observations conditioned on its observations and on the fact that some...

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: The case against degrowth, published by Stijn on December 30, 2021 on The Effective Altruism Forum.

Degrowth can be defined as the planned reduction (through policy interventions) of production and consumption in high-income countries, in order to reduce the environmental impact. If the production and consumption is measured in terms of resource throughput, we are talking about resource degrowth. If it is measured in terms of GDP (economic wealth), we can call it economic degrowth.

This article argues why campaigning for degrowth is ineffective, and can even be counterproductive or harmful. Instead of degrowth of the economy, increasing technological innovation (research and development of clean tech) is more effective.

The ImPACT equation

To understand degrowth, we can start with the ImPACT equation, given as:

Im=PxAxCxT,

with Im the impact on the environment (e.g. kg CO2 emissions, decrease in biodiversity, level of pollution), P the population size (number of consumers), A the affluence or economic activity per capita (dollar economic wealth created per person or dollar GDP per person), C the consumption intensity of resources (e.g. kWh energy per dollar, m² land per dollar or kg minerals per dollar GDP), and T the translation factor that translates resource use in environmental impact (e.g. kg CO2 emissions per kWh energy used, decrease in ecosystem quality per m² land used). The total greenhouse gas emission is the product of four factors: the number of people times the average amount of dollars income per person times the average amount of energy used per dollar times the average emissions per unit energy used.

Degrowth assumes that the four factors in the ImPACT-equation are independent from each other (or more generally non-decreasing functions of the other factors). If that is the case, and as all four factors are positive, it is clear that one can reduce the environmental impact by reducing per capita GDP (the factor A) or resource use (the product of A and C).

With the impact equation, we can look for those factors whose reduction is the most effective way to reduce total environmental impact. We aim for the largest reduction in impact, preferably all the way down to zero.

Reducing population: the population degrowth objective

Reducing population size (the factor P) is the objective of population degrowthers or antinatalists. The only ethical method to reduce population size, is investing in voluntary family planning to reduce unwanted pregnancies. However, this measure has a limited potential: even if all unwanted pregnancies are avoided, population size will not decrease fast enough to meet climate targets. More drastic population reduction measures, for example by unvoluntary sterilization, are unethical and politically unfeasible. One cannot reach zero impact by reducing only the population size, except if one reduces the population to zero, which is definitely unfeasible.

Reducing population size may not only be ineffective, but could be harmful in some ways. First, there are the negative economic effects of population reductions. Fewer people means fewer brains and hence fewer new discoveries, inventions and solutions to problems. It means less specialization of work, less division of labor, which results in lower labor productivity and hence longer working hours (less leisure). It means having fewer customers and buyers, and hence lower incomes.

Second, there are negative ethical aspects of population reductions. Having fewer happy people is problematic according to some reasonable population ethical theories, such as total utilitarianism that maximizes the sum of welfare of everyone in the future. It is possible that future generations have higher welfare levels than us. If these future populations are smaller than what could have been, it means th...

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: Increased Availability and Willingness for Deployment of Resources for Effective Altruism and Long-Termism, published by Evan Gaensbauer on December 29, 2021 on The Effective Altruism Forum.

In the last few months, there have been several indicators of a dramatic increase of resources available from a number of organizations in effective altruism, in particular for the focus area of EA community/infrastructure building and long-termism.

This includes not only financial resources for typical grant-making but also for research fellowships and scholarships from newer funding sources, as well as different kinds of institutional support and career opportunities.

This list isn't a call for applications or requests at this time from any of the organizations in question. This is only a summary of recent developments so the EA community at large is aware of these opportunities and changes for the purpose of strategic decision-making.

Benjamin Todd, CEO of 80,000 Hours, made the case for why EA needs 'mega-projects' i.e., projects that can deploy up to $100 million per year.

The Centre for Effective Altruism (CEA) has dramatically grown in the last year, nearly doubling in staff.

Rethink Priorities has also doubled their team and is looking to expand further.

The EA Infrastructure Fund expects the total that will be granted in 2021 will exceed $5 million, more than 2.7x the total granted in 2020, and 1.3x has awarded in all previous years (2018-2020) combined, and that they expect to be able to make these funding needs.

The Survival and Flourishing Fund deployed approximately $19 million in 2021 vs. $5.4 million in 2020.

Vitalik Buterin, founder of the blockchain platform Ethereum, donated $25 million to the Future of Life Institute to support research activities such as Ph.D. scholarships.

Lightcone Infrastructure, now the parent organization for rationality community blog LessWrong, are hiring new staff with salaries of $150-200 thousand dollars.

The Effective Altruism Forum recently held a creative writing contest that rewarded $20 thousand total the winners.

AI safety and research company Anthropic has raised $124 million in a Series A funding round.

Community member Sam Bankman-Fried is now the richest person under 30 and the cryptocurrency exchange he founded, FTX, is now offering EA fellowships.

The CEA is launching Campus Centers at top universities and is expecting some of these centers to be spending millions of dollars per year within the next few years. Beyond this, local groups funding is becoming much more accessible from either the CEA or the Global Challenges Project.

Open Philanthropy is more heavily investing in outreach projects and has announced Early Career Funding for Improving the Long-Term Future, biosecurity scholarships and tech policy fellowships.

Popular blogger and EA community member Scott Alexander through his blog, Astral Codex Ten, and the community around it is overseeing $1.5 million worth of grant-making.Thanks to Chris Leong for helping collate the above information and resources.

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: Personal Response to Omicron, published by jefftk on December 30, 2021 on LessWrong.

Throughout the pandemic, our household has been on the careful end. We started preparing in

February. 2020, isolated thoroughly from

March to

July, moderately over the summer, and then thoroughly again from

October until all the adults in our house were

vaccinated. Because I was

immunocompromised, we had a

newborn, and kids under five couldn't get vaccinated yet, however, our family's 2021 was still a lot like 2020. Then, in doing

Christmas holidays with family I was very careful individually in the two weeks leading up to it and I was the one

working on precautions and making sure we had enough tests: some of the people I really wanted to spend Christmas with are at elevated risk.

I wrote last week about how there are a range of paths we could take with Omicron as a society, but now that. Christmas is over what am I going to do personally?

Well, what's the weather out there?

That's a lot of covid in the sewer, 5x the previous peak and still going up.

I think if it were critical that I or a housemate didn't get Omicron, it would be possible, but very difficult. We would need to go back to isolating the way we were in Fall 2020, treating vaccinated people as about as risky as unvaccinated people, pulling the kids out of school, and never going indoors anywhere. Given the likely effects of contracting covid as a child or boosted adult, this is not worth it for us or I suspect most people.

Instead, I expect that I, the people in my house, and pretty much everyone who doesn't take intense and careful effort to avoid it will be exposed to Omicron at some point in the next ~month. It's not a good thing, but it is what it is. Afterward, people's immune systems will have had yet another covid exposure, and I expect cases to go low until next fall. So I'm not going to stress about it: I'll follow official guidance and mask regulations, cheerfully go along with precautions others need, and test+isolate when sick, but I'm not going to go above and beyond to attempt to reduce spread the way I did for earlier parts of the pandemic.

I'm thinking of Omicron as the first wave of the endemic phase of covid-19. While "it's just the flu" was completely wrong in 2020, in. 2022 the situation has changed enough of that "what would I do in flu season" is now a good guide.

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