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 5/12/22: Other Priorities, published by Zvi on May 12, 2022 on LessWrong.

There is zero funding for dealing even with the current pandemic, let alone preventing the next one. The FDA not only is in no hurry to approve a vaccine for children, the new highlight is its focus on creating a dire shortage of specialty baby formula. Covid doesn’t kill children, merely causing governments to mandate they not get to have their childhoods, but 40% of formula being out of stock is a much more directly and physically dangerous situation. The FDA has a history of killing children via not letting them have the nutrition they need to survive, last time it was an IV formulation that was incomplete but couldn’t be updated for years, so we shouldn’t act all surprised when this threatens to happen again.

Also Covid is still a thing. New subvariants of Omicron continue to slowly gain dominance and case numbers are increasing. The jump this week wasn’t as big as the headline number but it was still big, and is backed up by my local anecdata. There’s once again a bunch of Covid out there. The key is whether we can continue to get on with life and not fall into the trap of doing expensive prevention that doesn’t accomplish anything. So far, so good.

This intentionally still excludes China. News on that front has slowed dramatically, I plan to write up the last few weeks into a post anyway relatively soon.

Executive Summary

Case numbers up, although death numbers still down.

FDA causing specialty baby formula shortage crisis.

Still no funding for anything pandemic related.

Let’s run the numbers.

The Numbers

Predictions

Prediction from last week: 400,000 cases (+22%) and 2,720 deaths (+10%?)

Results: 518,048 cases (+41%) and 2,084 deaths (-6%)

Prediction for next week: 640,000 cases (+23%) and 2,100 deaths (+1%)

I’d love to go back and do a study of my predictions at some point if I find the bandwidth, and in particular on whether I should be ‘sticking to my guns’ more when a change doesn’t make a lot of sense. Often a blip causes an adjustment in the wrong direction and this week feels like I ‘fell for’ that trap once again, but that implies we know it wasn’t a legitimate change.

I also suspected there was some sort of two week cycle, and, well, yeah. It’s Florida, which is literally only reporting numbers every two weeks in the Wikipedia data this past month. Flat out 0 cases last week and three weeks ago. Should have caught that, and it explains the drop in the South very cleanly. Not sure how I missed it before.

Even adjusting for that, the jump this week is large – we got the additional 13% we ‘missed’ last week and also the adjustment I made based on the miss. Early signs from a few weeks ago that things would soon stabilize appear to have been wrong. Cases are likely to continue to rise for a bit.

I don’t expect the +41% to happen again, but as new subvariants spread this seems like a time to expect large week over week case growth, so even with Florida maybe not reporting at all (need to hedge on this), I’m predicting a +23% jump.

Deaths

This is even more dramatic than it looks, with Florida effectively shifting over 100 deaths from last week forward into this one. The Northeast and New York lines do indicate that deaths will start going back up again at some point, but for now the ‘fading out’ of with-Covid cases and old cases continues to drop reported numbers. My guess is that ‘real’ Covid deaths have been rising, so once the backlogs are sufficiently cleared we should see deaths rise again. But we also need to adjust for Florida likely reporting nothing.

Question for everyone: Should I instead be predicting the ‘real’ answer and committing to smoothing out Florida if they don’t report in a given week? I don’t want to do that without thinking more about it first but I’m gues...

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: Request for proposals: Help Open Philanthropy quantify biological risk, published by djbinder on May 12, 2022 on The Effective Altruism Forum.

Open Philanthropy is seeking proposals from those interested in contributing to a research project informing and estimating biosecurity-relevant numbers and ‘base rates’. We welcome proposals from both research organizations and individuals (at any career stage, including undergraduate and postgraduate students). The work can be structured via contract or grant.

The application deadline is May 29th.

Background

How likely is a biological catastrophe? Do the biggest risks come from states or terrorists? Accidents or intentional attacks?

These parameters are directly decision-relevant for Open Philanthropy’s biosecurity and pandemic preparedness strategy. They determine the degree to which we prioritize biosecurity compared to other causes, and inform how we prioritize different biosecurity interventions (e.g. do we focus on lab safety to reduce accidents, or push for better DNA synthesis screening to impede terrorists?).

One way of estimating biological risk that we do not recommend is ‘threat assessment’—investigating various ways that one could cause a biological catastrophe. This approach may be valuable in certain situations, but the information hazards involved make it inherently risky. In our view, the harms outweigh the benefits in most cases.

A second, less risky approach is to abstract away most biological details and instead consider general ‘base rates’. The aim is to estimate the likelihood of a biological attack or accident using historical data and base rates of analogous scenarios, and of risk factors such as warfare or terrorism. A few examples include:

Estimating the rate at which states or terrorist groups have historically sought biological, chemical, nuclear, or radiological weapons.

Forecasting the risk of great power war over the next 50 years (combining historical data with current geopolitical trends).

Estimating the rate at which lab leaks have occurred in state programs.

Enumerating possible ‘phase transitions’ that would cause a radical departure from relevant historical base rates, e.g. total collapse of the taboo on biological weapons, such that they become a normal part of military doctrine.

This information allows us to better estimate the probability of biological catastrophe in a variety of scenarios, with some hypothetical concrete examples described in the next section.

The biosecurity team at Open Philanthropy (primarily Damon) is currently working on developing such models. However, given the broad scope of the work, we would be keen to see additional research on this question. While we are interested in independent models that attempt to estimate the overall risk of a global biological catastrophe (GCBR), we are particularly keen on projects that simply collect huge amounts of relevant historical data, or thoroughly explore one or more sub-questions.

One aspect of this approach we find particularly exciting is that it is threat-agnostic and thus relevant across a wide range of scenarios, foreseen and unforeseen. It also helps us to think about the misuse of other dangerous transformative technologies, such as atomically precise manufacturing or AI.

We are therefore calling for applications from anyone who would be interested in spending up to the next four months (or possibly longer) helping us better understand these aspects of biorisk. We could imagine successful proposals from many different backgrounds—for example, a history undergraduate looking for a summer research project, a postdoc or superforecaster looking for part-time work, or a quantitative research organization with an entire full-time team.

What is our goal?

We are interested in supporting work that will help us better quanti...

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: Do you want to work in the new Boston EA office at Harvard Square?, published by Kaleem on May 12, 2022 on The Effective Altruism Forum.

TLDR: We're opening a Constellation/Trajan style office in Boston in 2023 and we're interested in user feedback.

We – Kaleem Ahmid, CEA’s new Project Manager for the Boston Office, supervised by Jonathan Michel, CEA’s Head of Property – are managing the creation of an office space in Harvard Square in Cambridge, Massachusetts. It is currently projected to open in the first half of 2023. The (currently unnamed) office will consist of a space dedicated to EA outreach, and a productivity-optimized, professional, interdisciplinary co-working space.

Our current plan includes:

Student outreach: One section of the space will be outreach-focused, with a coworking/event space and various meeting spaces for students at Harvard and other Boston-area schools and offices for full-time outreach professionals.

Professional coworking space: One section will be dedicated to full-time professionals, in the style of Trajan House or Constellation, with private offices, meeting and coworking spaces, and various amenities. We imagine this will accommodate ~40 people at any given time.

Note that there will be a reasonably clear separation between these two spaces, e.g. being on different floors.

We won’t be settling on the final group of individuals and orgs who will work in the office space for a while, and reserve the right to make changes to the mix as the space develops. However, if you are interested, we encourage you to read on.

If you might want to work in either of these spaces, please fill out our interest form ASAP for two reasons:

We are in the early design stages, and many of the decisions we will make benefit from potential-user input.

As we approach the opening of the space, we will reach out to people who fill out this interest form as we build our initial community, including both full-time office occupants and tentative plans for a semester-long fellowship to work out of the office and talk to a bunch of students. So if you express interest early, your application will be considered when there are still plenty of spots to fill. Don’t wait too long!

We’re also interested in hearing about other people who you think we should consider reaching out to, or who should fill out this form. Feel free to tell us at the end of filling it out yourself, or send me a DM. Some examples of people we’d be interested in hearing from:

You are a dedicated and knowledgeable EA and would like to work from our office to engage with others members of the community.

You are working for an EA org, starting a new EA org, (in the area) and you are looking for an office.

You’re an experienced community builder and are interested in building the EA community at Harvard/MIT, and want to use the dedicated outreach space at the office to do so.

You’re working on an EA project which would benefit from being housed in a space full of other EAs or in proximity with university students.

Why a Boston/Cambridge, MA hub for outreach and EA more broadly?

The office will be located in the only two mile radius on earth home to two of the world’s top 5 universities, in the center of Harvard’s campus (less than 5 minutes walking-distance from the Kennedy School, the Law School, the Science Center, and most undergraduate residences), as well as being within half an hour of other well-regarded universities like Tufts, Boston University, Brandeis, Northeastern, Boston College, Suffolk University, Emerson, UMass Boston, and is right next to a subway station on the MBTA red line (15 minutes to MIT).

EA community building is a high-impact endeavor. Increasing the chances of Harvard and MIT students to pursue high-impact careers seems like a very valuable thing to do. A world-class office ...

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: Introduction to the sequence: Interpretability Research for the Most Important Century, published by Evan R. Murphy on May 12, 2022 on The AI Alignment Forum.

This is the first post in a sequence exploring the argument that interpretability is a high-leverage research activity for solving the AI alignment problem.

This post contains important background context for the rest of the sequence. I'll give an overview of one of Holden Karnofksy's (2022) "Important, actionable research questions for the most important century" which is the central question we'll be engaging with in this sequence. I'll also define some terms and compare this sequence to existing works.

If you're already very familiar with Karnofsky (2022) and interpretability, then you can probably skip to the second post in this sequence: Interpretability’s Alignment-Solving Potential: Analysis of 7 Scenarios

The Alignment Research Activities Question

This sequence is being written as a direct response to the following question from Karnofsky (2022):

“What relatively well-scoped research activities are particularly likely to be useful for longtermism-oriented AI alignment?” (full question details)

I'll refer to this throughout the sequence as the Alignment Research Activities Question.

Context on the question and why it matters

In the details linked above for the Alignment Research Activities Question, Holden first discusses two categories of alignment research which are lacking in one way or another. He then presents a third category with some particularly desirable properties:

“Activity that is [1] likely to be relevant for the hardest and most important parts of the problem, while also being [2] the sort of thing that researchers can get up to speed on and contribute to relatively straightforwardly (without having to take on an unusual worldview, match other researchers’ unarticulated intuitions to too great a degree, etc.)”

He refers to this as "category (3)", but I'll use the term High-leverage Alignment Research since it's more descriptive and we'll be referring back to this concept often throughout the sequence.

We want to know more about which alignment research is in this category. Why? Further excerpts from Karnofsky (2022) to clarify:

“I think anything we can clearly identify as category (3) [that is, High-leverage Alignment Research] is immensely valuable, because it unlocks the potential to pour money and talent toward a relatively straightforward (but valuable) goal.[...]I think there are a lot of people who want to work on valuable-by-longtermist-lights AI alignment research, and have the skills to contribute to a relatively well-scoped research agenda, but don’t have much sense of how to distinguish category (3) from the others.

There’s also a lot of demand from funders to support AI alignment research. If there were some well-scoped and highly relevant line of research, appropriate for academia, we could create fellowships, conferences, grant programs, prizes and more to help it become one of the better-funded and more prestigious areas to work in.

I also believe the major AI labs would love to have more well-scoped research they can hire people to do."

I won't be thoroughly examining other research directions besides interpretability, except in cases where a hypothetical interpretability breakthrough is impacting another research direction toward a potential solution to the alignment problem. So I don't expect this sequence to produce a complete comparative answer to the Alignment Research Activities Question.

But by investigating whether interpretability research is High-leverage Alignment Research, I hope to put together a fairly comprehensive analysis of interpretability research that could be useful to people considering investing their money or time into it. I also hope that someone t...

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: Deepmind's Gato: Generalist Agent, published by Daniel Kokotajlo on May 12, 2022 on LessWrong.

From the abstract, emphasis mine:

The agent, which we refer to as Gato, works as a multi-modal, multi-task, multi-embodiment generalist policy. The same network with the same weights can play Atari, caption images, chat, stackblocks with a real robot arm and much more, deciding based on its context whether to output text, joint torques, button presses, or other tokens.

(Will edit to add more as I read. ETA: 1a3orn posted first.)

It's only 1.2 billion parameters. (!!!) They say this was to avoid latency in the robot control task.

It was trained offline, purely supervised, but could in principle be trained online, with RL, etc

Performance results:

The section on broader implications is interesting. Selected quote:

In addition, generalist agents can take actions in the the physical world; posing new challenges that may require novel mitigation strategies. For example, physical embodiment could lead to users anthropomorphizing the agent, leading to misplaced trust in the case of a malfunctioning system, or be exploitable by bad actors. Additionally, while cross-domain knowledge transfer is often a goal in ML research, it could create unexpected and undesired outcomes if certain behaviors (e.g. arcade game fighting) are transferred to the wrong context. The ethics and safety considerations of knowledge transfer may require substantial new research as generalist systems advance. Technical AGI safety (Bostrom, 2017) may also become more challenging when considering generalist agents that operate in many embodiments. For this reason, preference learning, uncertainty modeling and value alignment (Russell, 2019) are especially important for the design of human-compatible generalist agents.

It may be possible to extend some of the value alignment approaches for language (Kenton et al., 2021; Ouyang et al., 2022) to generalist agents. However, even as technical solutions are developed for value alignment, generalist systems could still have negative societal impacts even with the intervention of well-intentioned designers, due to unforeseen circumstances or limited oversight (Amodei et al., 2016). This limitation underscores the need for a careful design and a deployment process that incorporates multiple disciplines and viewpoints.

They also do some scaling analysis and yup, you can make it smarter by making it bigger.

What do I think about all this?

Eh, I guess it was already priced in. I think me + most people in the AI safety community would have predicted this. I'm a bit surprised that it works as well as it does for only 1.2B parameters though.

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: "A Generalist Agent": New DeepMind Publication, published by 1a3orn on May 12, 2022 on LessWrong.

Linkpost for "A Generalist Agent"

Abstract:

"Inspired by progress in large-scale language modeling, we apply a similar approach towards building a single generalist agent beyond the realm of text outputs. The agent, which we refer to as Gato, works as a multi-modal, multi-task, multi-embodiment generalist policy. The same network with the same weights can play Atari, caption images, chat, stack blocks with a real robot arm and much more, deciding based on its context whether to output text, joint torques, button presses, or other tokens. In this report we describe the model and the data, and document the current capabilities of Gato"

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: Bad Omens in Current Community Building, published by Theo Hawking on May 12, 2022 on The Effective Altruism Forum.

Community building has recently had a surge in energy and resources. It scales well, it’s high leverage, and you can get involved in it even if six months ago you’d never heard of Effective Altruism. Having seen how hard some people are rowing this boat, I’d like to see if I can’t steer it a bit.

The tl;dr is:

Some current approaches to community building especially in student groups are driving away great people

These approaches involve optimising for engaging a lot of new people in a way that undermines good epistemics, and trades off against other goals which are harder to measure but equally important (cf Goodhart’s Law)

I argue that this is a top priority because the people that these approaches drive away are in many cases the people EA needs the most.

I’ll start by describing why I and several friends of mine did not become EAs.

Then I’ll lay out my sense of what EA needs and what university community building is trying to achieve. I’ll also discuss things that I have encountered in community building that I’ve found troubling.

After that I’ll give my model for how these approaches to community building might be causing serious problems

Finally I’ll explain why I think that this issue in particular needs to be prioritised, and what I think could be done about it.

Part 1 - Reasons I and others did not become an EA

1

I was talking to a friend a little while ago who went to an EA intro talk and is now doing one of 80,000 Hours' recommended career paths, with a top score for direct impact. She’s also one of the most charismatic people I know, and she cares deeply about doing good, with a healthy practical streak.

She’s not an EA, and she’s not going to be. She told me that she likes the concept and the framing, and that since the intro talk she’s often found that when faced with big ethical questions it’s useful to ask “what would an EA do”. But she’s not an EA. Off the back of the intro talk and the general reputation as she perceived it, she got the sense that EA was a bit totalising, like she couldn’t really half-join, so she didn’t. Still, she enjoys discussing the concept of it with me, and she’s curious to hear more about AI.

Certainly there are some professions, like AI safety, where one person going all in is strikingly better than a lot of people who are only partly engaged, but in her area I don’t think this applies. I’ll build on this later.

2

A friend of mine at a different university attended the EA intro fellowship and found it lacking. He tells me that in the first session, foundational arguments were laid out, and he was encouraged to offer criticism. So he did. According to him, the organisers were grateful for the criticism, but didn’t really give him any satisfying replies. They then proceeded to build on the claims about which he remained unconvinced, without ever returning to it or making an effort to find an answer themselves.

He recently described something to me as ‘too EA’. When I pushed him to elaborate, what he meant was something like ‘has the appearance of inviting you to make your own choice but is not-so-subtly trying to push you in a specific direction’.

3

Another friend of mine is currently working on Bayesian statistical inference, but has an offer to work as a quantitative trader. He hopes to donate some of his income to charity. He does not want to donate to EA causes, or follow EA recommendations, and in fact he will pretty freely describe EA as a cult. He has not, as far as I know, attended any EA events. He has already made his mind up.

As far as I can tell, this is the folk wisdom among mathematicians in my university: I’ve heard the rough sentiment expressed several times, usually in response to people saying ...

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: Becoming an EA Architect: My First Month as an Independent Researcher, published by Tereza Flidrova on May 12, 2022 on The Effective Altruism Forum.

I am on the mission of figuring out how to use my education and skills as an architect and urban designer to pursue a high-impact career. For a long time, I did not think there were many intersections between EA and architecture. However, this spring I am trying to prove my past self wrong. Having come to the Bahamas as an FTX EA fellow, I have quit my job in urban design in Oxfordshire to devote all my time and resources to potentially making a major career shift into the EA/longtermist spaces.

This post (my first one on EAF, so bear with me) is about the first month of my exploration. By writing it, I hope to gain insights from the EA community, connect with like-minded people, gather feedback or advice, and perhaps inspire people with similar skillsets to come and join me on my mission.

So far, I have identified 4+ major categories where my skills might be useful and spend different amounts of time pursuing them.

They are:

Refuges from biological and nuclear threats

Design of spaces for disease mitigation

Office space design

Charter Cities

All else (so far, things like graphic design or Street Votes)

1. Refuges

Building refuges to protect from biological and nuclear threats could increase chances that humanity would recover from a global catastrophe (Beckstead 2015). It seems plausible that devoting my time to help kickstart the project of building refuges (or superbunkers, as described by Linch and Ajay in their post) could be valuable. Linch offered me mentorship and resources (as part of the FTX Regranting Program) to explore ways to fit in. Spending last month on initial learning and research, I plan to next work on:

a comprehensive Superbunkers Precedents Report (including examples from existing governmental and private bunkers, submarines, remote research stations and experiments, space explorations, peoples living in seclusion, etc. and comparing things like costs, size and level of protection); and

creating materials (currently imagining 10ish slides very clearly stating and illustrating the main points) for PR and marketing purposes to be used to contact potential suppliers and funders to get a better understanding of what’s feasible/achievable;

to maximise my learning experience while potentially starting to produce outcomes. In the longer term and the most optimistic scenario, I hope that helping to progress work on superbunkers might lead to attracting a potential CEO that could take ownership of the project (as Linch and Ajay described in their post from last month, this will be a crucial milestone for superbunkers).

2. Spaces for disease mitigation

Nonpharmaceutical Interventions (NPIs) such as designing buildings for sustainable disease mitigation can play an important role in helping to slow the spread of illnesses and prevent future pandemics (“Nonpharmaceutical Interventions (NPIs)” 2020). After the COVID-19 pandemic, there might be a capacity to implement new design solutions to provide the required protection from virus attacks, adding more layers to the defence system of our built environment (Megahed and Ghoneim 2020).

It seems that the design and user experience of such interventions could be an impactful cause to focus on during the upcoming years. My discussions with people in the space so far indicate that designers or architects could aid researchers to implement interventions into the built environment and public spaces in ways that make them more effective. Websites like Building Survey, developed by Arvie Violett to assist different sizes of buildings with different budgets and preferences in choosing the most suitable interventions, show an example of how architects could contribute.

I b...

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: Stuff I might do if I had covid, published by KatjaGrace on May 11, 2022 on LessWrong.

In case anyone wants a rough and likely inaccurate guide to what I might do if I had covid to mitigate it, I looked into this a bit recently and wrote notes. It’s probably better than if one’s plan was to do less than a few hours of research, but is likely flawed all over the place and wasn’t written with public sharing in mind, and um, isn’t medical advice:

Here’s a Google doc version, where any comments you leave might be seen by the next person looking (and you might see comments added by others).

Here’s a much longer doc with the reasoning, citations and more comments.

(I continue to guess that long covid is worth avoiding.)

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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: An Inside View of AI Alignment, published by Ansh Radhakrishnan on May 11, 2022 on LessWrong.

I started to take AI Alignment seriously around early 2020. I’d been interested in AI and machine learning in particular since 2014 or so, taking several online ML courses in high school and implementing some simple models for various projects. I leaned into the same niche in college, taking classes in NLP, Computer Vision, and Deep Learning to learn more of the underlying theory and modern applications of AI, with a continued emphasis on ML. I was very optimistic about AI capabilities then (and still am) and if you’d asked me about AI alignment or safety as late as my sophomore year of college (2018-2019), I probably would have quoted Steven Pinker or Andrew Ng at you.

Somewhere in the process of reading The Sequences, portions of the AI Foom Debate, and texts like Superintelligence and Human Compatible, I changed my mind. Some 80,000 hours podcast episodes were no doubt influential as well, particularly the episodes with Paul Christiano. By late 2020, I probably took AI risk as seriously as I do today, believing it to be one of the world’s most pressing problems (perhaps the most) and was interested in learning more about it.

I binged most of the sequences on the Alignment Forum at this point, learning about proposals and concepts like IDA, Debate, Recursive Reward Modeling, Embedded Agency, Attainable Utility Preservation, CIRL etc. Throughout 2021 I continued to keep a finger on the pulse of the field: I got a large amount of value out of the Late 2021 MIRI Conversations in particular, shifting away from a substantial amount of optimism in prosaic alignment methods, slower takeoff speeds, longer timelines, and a generally “Christiano-ish” view of the field and more towards a “Yudkowsky-ish” position.

I had a vague sense that AI safety would eventually be the problem I wanted to work on in my life, but going through the EA Cambridge AGI Safety Fundamentals Course helped make it clear that I could productively contribute to AI safety work right now or in the near future. This sequence is going to be an attempt to explicate my current model or “inside view” of the field. These viewpoints have been developed over several years and are no doubt influenced by my path into and through AI safety research: for example, I tend to take aligning modern ML models extremely seriously, perhaps more seriously than is deserved, because of my greater amount of experience with ML compared to other AI paradigms.

I’m writing with the express goal of having my beliefs critiqued and scrutinized: there’s a lot I don’t know and no doubt a large amount that I’m misunderstanding. I plan on writing on a wide variety of topics: the views of various researchers, my understanding and confidence in specific alignment proposals, timelines, takeoff speeds, the scaling hypothesis, interpretability, etc. I also don’t have a fixed timeline or planned order in which I plan to publish different pieces of the model.

Without further ado, the posts that follow comprise Ansh’s (current) Inside View of AI Alignment.

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