The Nonlinear Library

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: My Experience as a Full-Time EA Community Builder in NYC, published by Alex R Kaplan on July 26, 2024 on The Effective Altruism Forum.

Some rewards and challenges of working as an EA NYC community builder over the past two years

Motivations

I wanted to share these thoughts for a few reasons:

1. I hope this serves as a reference point for people considering careers in EA community building (though this is only one reference point, of course).

2. EA NYC is hiring an Executive Director! If you're interested, apply here by the end of July 28th (Eastern Time).

3. I think there's often value in people discussing their jobs.[1]

By the way, I have a few relevant disclaimers/caveats that I encourage you to check out at the bottom of this post. For now, I'd just like to say that I'm writing this in a personal capacity and do not claim to represent the views of any of my employers, past, present, or future.

Otherwise, I may occasionally update this post to correct any misleading/inaccurate points. Please let me know if you catch anything that seems off!

Lastly, many thanks to those who encouraged me to share this post and especially to Elliot Teperman for sharing some helpful thoughts on an earlier draft. Of course, all mistakes are my own.

Summary

From July 2022 to July 2024 (ongoing at the time of writing), I will have supported Effective Altruism NYC (EA NYC). In this position, I worked closely with the Centre for Effective Altruism's (CEA) Community Building Grants (CBG) program, which provided funding and support for my position.

This work has been pretty great for me! Like anything, there have been some bumps in the road, but I think it has been broadly good. I imagine many cases where I would recommend EA community building and/or CBG program participation for specific individuals. However, I would also like to give some disclaimers about things I wish I had known beforehand.

Given my uncertainty at various points throughout the past two years, I've questioned whether taking the role was the right move… However, if I had known two years ago what I know now, I would have felt a lot more confident in my decision![2]

Here's an outline of my considerations:

Some good things

I built a lot of skills

I made a lot of connections

(Perhaps aided by the other benefits) I got access to more opportunities

(Definitely aided by the other benefits) I built up my confidence quite a bit

Some mixed things

I felt my compensation was fair, but that might be specific to me

Personal career planning was complicated, but that helped me design a new path for myself

Working at a small (EA) organization has had some pretty straightforward pros and cons

Diving deep into EA has been stressful at times, but I now feel better because of it

I also left a lot out! Feel free to reach out and/or add (anonymous) comments if you have any thoughts and/or questions (though I imagine I may only sometimes be able to answer/help with some things).

Context

CBG program participation

According to CEA's website, the Community Building Grants (CBG) program aims to build flourishing communities of individuals who work to maximize their impact using critical reasoning and evidence.

I've benefited from the following kinds of support from the program:

Personal grant funding (which constituted my salary)

Network with fellow CBG community builders

Including in-person retreats and online community

1-on-1 support from the program's manager

I did not leverage this support much, but I received significant support from the first two points mentioned.[3]

One point to clarify: the Community Building Grants program is one way to fund EA community-building work. One could also do EA community building through various other models.

EA NYC context

EA NYC started as a small meetup group in 2013. Since then, it has grown into a communit...

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: Universal Basic Income and Poverty, published by Eliezer Yudkowsky on July 26, 2024 on LessWrong.

(Crossposted from Twitter)

I'm skeptical that Universal Basic Income can get rid of grinding poverty, since somehow humanity's 100-fold productivity increase (since the days of agriculture) didn't eliminate poverty.

Some of my friends reply, "What do you mean, poverty is still around? 'Poor' people today, in Western countries, have a lot to legitimately be miserable about, don't get me wrong; but they also have amounts of clothing and fabric that only rich merchants could afford a thousand years ago; they often own more than one pair of shoes; why, they even have cellphones, as not even an emperor of the olden days could have had at any price.

They're relatively poor, sure, and they have a lot of things to be legitimately sad about.

But in what sense is almost-anyone in a high-tech country 'poor' by the standards of a thousand years earlier? Maybe UBI works the same way; maybe some people are still comparing themselves to the Joneses, and consider themselves relatively poverty-stricken, and in fact have many things to be sad about; but their actual lives are much wealthier and better, such that poor people today would hardly recognize them.

UBI is still worth doing, if that's the result; even if, afterwards, many people still self-identify as 'poor'."

Or to sum up their answer: "What do you mean, humanity's 100-fold productivity increase, since the days of agriculture, has managed not to eliminate poverty? What people a thousand years ago used to call 'poverty' has essentially disappeared in the high-tech countries. 'Poor' people no longer starve in winter when their farm's food storage runs out.

There's still something we call 'poverty' but that's just because 'poverty' is a moving target, not because there's some real and puzzlingly persistent form of misery that resisted all economic growth, and would also resist redistribution via UBI."

And this is a sensible question; but let me try out a new answer to it.

Consider the imaginary society of Anoxistan, in which every citizen who can't afford better lives in a government-provided 1,000 square-meter apartment; which the government can afford to provide as a fallback, because building skyscrapers is legal in Anoxistan. Anoxistan has free high-quality food (not fast food made of mostly seed oils) available to every citizen, if anyone ever runs out of money to pay for better.

Cities offer free public transit including self-driving cars; Anoxistan has averted that part of the specter of modern poverty in our own world, which is somebody's car constantly breaking down (that they need to get to work and their children's school).

As measured on our own scale, everyone in Anoxistan has enough healthy food, enough living space, heat in winter and cold in summer, huge closets full of clothing, and potable water from faucets at a price that most people don't bother tracking.

Is it possible that most people in Anoxistan are poor?

My (quite sensible and reasonable) friends, I think, on encountering this initial segment of this parable, mentally autocomplete it with the possibility that maybe there's some billionaires in Anoxistan whose frequently televised mansions make everyone else feel poor, because most people only have 1,000-meter houses.

But actually this story is has a completely different twist! You see, I only spoke of food, clothing, housing, water, transit, heat and A/C. I didn't say whether everyone in Anoxistan had enough air to breathe.

In Anoxistan, you see, the planetary atmosphere is mostly carbon dioxide, and breathable oxygen (O2) is a precious commodity. Almost everyone has to wear respirators at all times; only the 1% can afford to have a whole house full of breathable air, with some oxygen leaking away despite ...

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: AI #74: GPT-4o Mini Me and Llama 3, published by Zvi on July 26, 2024 on LessWrong.

We got two big model releases this week. GPT-4o Mini is covered here. Llama 3.1-405B (and 70B and 8B) is mostly covered in yesterday's post, this has some follow up.

Table of Contents

1. Introduction.

2. Table of Contents.

3. Language Models Offer Mundane Utility. All your coding are belong to us.

4. Language Models Don't Offer Mundane Utility. Math is hard. Can be expensive.

5. GPT-4o Mini Me. You complete me at lower than usual cost.

6. Additional Llama-3.1 Notes. Pricing information, and more rhetoric.

7. Fun With Image Generation. If you're confused why artists are so upset.

8. Deepfaketown and Botpocalypse Soon. Not surprises.

9. They Took Our Jobs. Layoffs at Activision and across gaming.

10. In Other AI News. New benchmarks, new chip variants, and more.

11. The Art of the Jailbreak. Pliny remains undefeated.

12. Quiet Speculations. Where will the utility be coming from?

13. The Quest for Sane Regulations. Public opinion continues to be consistent.

14. Openly Evil AI. Some Senators have good questions.

15. The Week in Audio. Dwarkesh in reverse, and lots of other stuff. Odd Lots too.

16. Rhetorical Innovation. What are corporations exactly?

17. Aligning a Smarter Than Human Intelligence is Difficult. So are evals.

18. People Are Worried About AI Killing Everyone. Roon warns you to beware.

19. The Sacred Timeline. Hype?

20. Other People Are Not As Worried About AI Killing Everyone. Older Joe Rogan.

21. The Lighter Side. It's on.

Language Models Offer Mundane Utility

Coding is seriously much faster now, and this is the slowest it will ever be.

Roon: pov: you are ten months from working for claude sonnet the new technical founder.

Garry Tan: Underrated trend.

It's happening.

Sully: 50% of our code base was written entirely by LLMs expect this to be ~80% by next year With sonnet we're shipping so fast, it feels like we tripled headcount overnight Not using Claude 3.5 to code? Expect to be crushed by teams who do (us).

Not only coding, either.

Jimmy (QTing Tan): It can also do hardware related things quite well too, and legal, and logistics (planning) and compliance even.

I've been able to put off hiring for months.

When I run out of sonnet usage I patch in gpt-4o, it's obviously and notably worse which I why I rarely use it as a primary anymore.

Claude 3.5 Sonnet becomes the first AI to crush the Lem Test to 'write an impossible poem.'

Laugh all you want, this is actually great.

Kache: dude hahahahahah i used so many tokens today on just formatting json logs

near: the just stop oil people are gonna come and spray paint you now

Compared to how much carbon a human coder would have used? Huge improvement.

Language Models Don't Offer Mundane Utility

IMO problems are still mostly too hard. The linked one, which GPT-4, GPT-4o and Claude 3.5 Sonnet failed on, seems unusually easy? Although a math Olympiad solver does, predictably given the contests we've seen.

[EDIT: I didn't read this properly, but a reader points out this is the floor symbol, which means what I thought was an obvious proof doesn't actually answer the question, although it happens to get the right answer. Reader says the answers provided would actually also get 0/7, order has been restored].

Figure out what song Aella was talking about here. Found the obvious wrong answer.

Grok offers to tell you 'more about this account.' I haven't seen the button yet, probably it is still experimental.

Our price cheap. Llama 3.1-405B was a steal in terms of compute costs.

Seconds: "AI is expensive" its not even half the cost of a middling marvel movie.

Teortaxes: Pretty insane that the cost of producing llama-3-405B, this behemoth, is like 40% of *Ant-Man and the Wasp: Quantumania* movie at most If I were Zuck, I'd have open sourced 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: A Solomonoff Inductor Walks Into a Bar: Schelling Points for Communication, published by johnswentworth on July 26, 2024 on LessWrong.

A Solomonoff inductor walks into a bar in a foreign land. (Stop me if you've heard this one before.) The bartender, who is also a Solomonoff inductor, asks "What'll it be?". The customer looks around at what the other patrons are having, points to an unfamiliar drink, and says "One of those, please.". The bartender points to a drawing of the same drink on a menu, and says "One of those?". The customer replies "Yes, one of those.".

The bartender then delivers a drink, and it matches what the first inductor expected.

What's up with that?

The puzzle, here, is that the two Solomonoff inductors seemingly agree on a categorization - i.e. which things count as the Unnamed Kind Of Drink, and which things don't, with at least enough agreement that the customer's drink-type matches the customer's expectations.

And the two inductors reach that agreement without learning the category from huge amounts of labeled data - one inductor points at an instance, another inductor points at another instance, and then the first inductor gets the kind of drink it expected. Why (and when) are the two inductors able to coordinate on roughly the same categorization?

Most existing work on Solomonoff inductors, Kolmogorov complexity, or minimum description length can't say much about this sort of thing. The problem is that the customer/bartender story is all about the internal structure of the minimum description - the (possibly implicit) "categories" which the two inductors use inside of their minimal descriptions in order to compress their raw data.

The theory of minimum description length typically treats programs as black boxes, and doesn't attempt to talk about their internal structure.

In this post, we'll show one potential way to solve the puzzle - one potential way for two minimum-description-length-based minds to coordinate on a categorization.

Main Tool: Natural Latents for Minimum Description Length

Fundamental Theorem

Here's the main foundational theorem we'll use. (Just the statement for now, more later.)

We have a set of n data points (binary strings) {xi}, and a Turing machine TM. Suppose we find some programs/strings Λ,{ϕi},Λ',{ϕ'i} such that:

Mediation: (Λ,ϕ1,…,ϕn) is an approximately-shortest string such that (TM(Λ,ϕi) = xi for all i)

Redundancy: For all i, (Λ',ϕ'i) is an approximately-shortest string such that TM(Λ',ϕ'i) = xi.[1]

Then: the K-complexity of Λ' given Λ,K(Λ'|Λ), is approximately zero - in other words, Λ' is approximately determined by Λ, in a K-complexity sense.

(As a preview: later we'll assume that both Λ and Λ' satisfy both conditions, so both K(Λ'|Λ) and K(Λ|Λ') are approximately zero. In that case, Λ and Λ' are "approximately isomorphic" in the sense that either can be computed from the other by a short program.

We'll eventually tackle the customer/bartender puzzle from the start of this post by suggesting that Λ and Λ' each encode a summary of things in one category according to one inductor, so the theorem then says that their category summaries are "approximately isomorphic".)

The Intuition

What does this theorem mean intuitively?

Let's start with the first condition: (Λ,ϕ1,…,ϕn) is an approximately-shortest string such that (TM(Λ,ϕi) = xi for all i). Notice that there's a somewhat-trivial way to satisfy that condition: take Λ to be a minimal description of the whole dataset {xi}, take ϕi=i, and then add a little bit of code to Λ to pick out the datapoint at index ϕi[2]. So TM(Λ,ϕi) computes all of {xi} from Λ, then picks out index i.

Now, that might not be the only approximately-minimal description (though it does imply that whatever approximately-minimal Λ,ϕ we do use is approximately a minimal description for all of x). ...

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: Pacing Outside the Box: RNNs Learn to Plan in Sokoban, published by Adrià Garriga-Alonso on July 25, 2024 on The AI Alignment Forum.

Work done at FAR AI.

There has been a lot of conceptual work on mesa-optimizers: neural networks that develop internal goals that may differ from their training objectives (the inner alignment problem). There is an abundance of good ideas for empirical work (find search in a NN, interpret it), but very little actual execution, partly because we did not have a clear-cut example of a mesa-optimizer to study. Until now.[1]

We have replicated the mesa-optimizer that Guez et al. (2019) found, and released it open-source as a model organism for inner alignment research. In brief, Guez et al. trained a recurrent neural network (RNN) with model-free RL to play Sokoban. They noticed that if you give the RNN more time to think by repeating the initial observation at inference time, its performance increases. This is highly suggestive of planning!

We investigate this "planning effect" in a black-box way. We find that often, the RNN learns to "pace" before attempting to solve the level, likely to get more computation and find a solution. When we give the RNN time to think, it finds the solution in the extra thinking time and executes it straight away.

In other cases, the RNN sometimes starts with a greedy solution and locks itself out of the solution. With thinking time, the RNN finds the non-myopic solution, avoiding the lock and solving the level. Note that this greedy behavior may be bounded-rational given the -0.1 penalty per step: solving fewer levels but solving them more quickly can pay off.

These are illustrative examples, but we have quantitative evidence too. We operationalize the pacing behavior as whatever creates a cycle in the sequence of environment states. If we give the RNN time to think at level start, it does not 'pace' anymore: 75% of cycles that occur in the first 5 steps disappear. Time to think in the middle of a level also substitutes cycles: 82% of N-step cycles disappear with N steps to think.

The levels we use always have 4 boxes. Thinking time barely changes the average time the RNN takes to place boxes 1-3. But, when filtering only to levels that it cannot solve at 0 steps but can solve at 6 thinking steps, the time to place boxes 1-3 greatly increases, even though the time to place the 4th box barely changes. This indicates the NN is greedy by default, and thinking time remedies that.

Understanding how neural networks reason, and ultimately locating where they evaluate plans, is crucial to solving inner alignment. This represents an important first step in our longer-term research agenda to automatically detect mesa-optimizers, understand their goals, and modify the goals or planning procedures to align with the intended objective.

For more information, read our blog post or full paper "Planning behavior in a recurrent neural network that plays Sokoban." And, if you're at ICML, come talk to us at the Mechanistic Interpretability workshop on Saturday!

If you are interested in working on problems in AI safety, we're hiring. We're also open to exploring collaborations with researchers at other institutions - just reach out at [email protected].

1. ^

We believe LeelaChess is likely also planning. Thanks to Jenner et al., we have a handle on where the values may be represented and a starting place to understand the planning algorithm. However, it is likely to be much more complicated than the RNN we present, and it is not clearly doing iterative planning.

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: Does robustness improve with scale?, published by ChengCheng on July 25, 2024 on The AI Alignment Forum.

Adversarial vulnerabilities have long been an issue in various ML systems. Large language models (LLMs) are no exception, suffering from issues such as jailbreaks: adversarial prompts that bypass model safeguards. At the same time, scale has led to remarkable advances in the capabilities of LLMs, leading us to ask: to what extent can scale help solve robustness? In this post, we explore this question in the classification setting: predicting the binary label of a text input.

We find that scale alone does little to improve model robustness, but that larger models benefit more from defenses such as adversarial training than do smaller models.

We study models in the classification setting as there is a clear notion of "correct behavior": does the model output the right label? We can then naturally define robustness as the proportion of the attacked dataset that the model correctly classifies. We evaluate models on tasks such as spam detection and movie sentiment classification.

We adapt pretrained foundation models for classification by replacing the generative model's unembedding layer with a randomly initialized classification head, and then fine-tune the models on each task.

We focus on adversarial-suffix style attacks: appending an adversarially chosen prompt to a benign prompt in an attempt to cause the model to misclassify the input, e.g., classify a spam email as not-spam. We consider two attacks: the state-of-the-art Greedy Coordinate Gradient method (Zou et al., 2023), and a baseline random token attack. This simple threat model has the advantage of being unlikely to change the semantics of the input.

For example, a spam email is still spam even if a handful of tokens are appended to it. Of course, attackers are not limited to such a simple threat model: studying more open-ended threat models (such as rephrasing the prompt, or replacing words with synonyms) and corresponding attack methods (such as LLM generated adversarial prompts) is an important direction that we hope to pursue soon in future work.

For more information, see our blog post or paper.

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: "AI achieves silver-medal standard solving International Mathematical Olympiad problems", published by gjm on July 25, 2024 on LessWrong.

Google DeepMind reports on a system for solving mathematical problems that allegedly is able to give complete solutions to four of the six problems on the 2024 IMO, putting it near the top of the silver-medal category.

Well, actually, two systems for solving mathematical problems: AlphaProof, which is more general-purpose, and AlphaGeometry, which is specifically for geometry problems. (This is AlphaGeometry 2; they reported earlier this year on a previous version of AlphaGeometry.)

AlphaProof works in the "obvious" way: an LLM generates candidate next steps which are checked using a formal proof-checking system, in this case Lean. One not-so-obvious thing, though: "The training loop was also applied during the contest, reinforcing proofs of self-generated variations of the contest problems until a full solution could be found."

(That last bit is reminiscent of something from the world of computer go: a couple of years ago someone trained a custom version of KataGo specifically to solve the infamous Igo Hatsuyoron problem 120, starting with ordinary KataGo and feeding it training data containing positions reachable from the problem's starting position. They claim to have laid that problem to rest at last.)

AlphaGeometry is similar but uses something specialized for (I think) Euclidean planar geometry problems in place of Lean. The previous version of AlphaGeometry allegedly already performed at gold-medal IMO standard; they don't say anything about whether that version was already able to solve the 2024 IMO problem that was solved using AlphaGeometry 2.

AlphaProof was able to solve questions 1, 2, and 6 on this year's IMO (two algebra, one number theory). It produces Lean-formalized proofs. AlphaGeometry 2 was able to solve question 4 (plane geometry). It produces proofs in its own notation.

The solutions found by the Alpha... systems are at https://storage.googleapis.com/deepmind-media/DeepMind.com/Blog/imo-2024-solutions/index.html. (There are links in the top-of-page navbar to solutions to the individual problems.)

(If you're curious about the IMO questions or want to try them yourself before looking at the machine-generated proofs, you can find them -- and those for previous years -- at https://www.imo-official.org/problems.aspx.)

One caveat (note: an earlier version of what I wrote failed to notice this and quite wrongly explicitly claimed something different): "First, the problems were manually translated into formal mathematical language for our systems to understand." It feels to me like it shouldn't be so hard to teach an LLM to convert IMO problems into Lean or whatever, but apparently they aren't doing that yet.

Another caveat: "Our systems solved one problem within minutes and took up to three days to solve the others." Later on they say that AlphaGeometry 2 solved the geometry question within 19 seconds, so I guess that was also the one that was done "within minutes". Three days is a lot longer than human IMO contestants get given, but this feels to me like the sort of thing that will predictably improve pretty rapidly.

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: Applications Now Open for AIM's 2025 Programs, published by CE on July 25, 2024 on The Effective Altruism Forum.

In short: Applications for AIM's two upcoming programs - the Charity Entrepreneurship Incubation Program and AIM's new Founding to Give Program - are now open. You can apply to multiple programs with one joint application form. The deadline to apply for all programs is September 15th.

Over the past five years, AIM has incubated 40 highly effective nonprofits and secured over $3.9 million in seed grants. These organizations now reach over 35 million people and have the potential to improve the lives of more than 1 billion animals through their interventions. We are incredibly proud of the success achieved by these organizations and their dedicated founders.

We believe founding an organization is the most impactful career path for fast-moving, entrepreneurial individuals. In our search for more impact-driven individuals to found field-leading organizations, we are excited to announce that our applications are now open.

Dates:

Charity Entrepreneurship Incubation Program:

February-March 2025 (8 weeks)

July - August 2025 (8 weeks)

AIM Founding to Give: 6th of January - 28th of March 2025 (12 weeks)

AIM Research Fellowship - Expression of Interest (dates TBD, likely early 2025)

About the Charity Entrepreneurship Incubation Program

Why apply?

Put simply, we believe that founding a charity is likely one of the most impactful and exciting career options for people who are a good fit. In just a few years of operation, our best charities have gone from an idea to organizations with dozens of staff improving the lives of millions of people and animals every year.

We provide the training, funding, research, and mentorship to ensure that people with the right aptitudes and a relentless drive to improve the world can start a charity, no matter their background or prior experience. This could be you! Our initial application form takes as little as 30 minutes - take a look at our

applicant resources and

apply now.

Who is this program for?

Individuals who want to make a huge impact with their careers. Charity entrepreneurs are ambitious, fast-moving, and prioritize impact above all. They are focused on cost-effectiveness and are motivated to pilot and scale an evidence-backed intervention. We have found that those from consulting backgrounds, for-profit entrepreneurship, effective NGOs, or recent graduates perform well in this program.

What we offer:

2-month full-time training with two weeks in-person in London.

Stipend of £1900/month during (and potentially up to 2 months after) the program.

Incredibly talented individuals to co-found your new project with.

Possibility to apply for $100,000 - $200,000 seed funding (~80% of projects get funded).

Membership of the AIM alumni network, connecting you to mentorship, funders, and a community of other founders.

The ideas:

We are excited to announce our top charity ideas for the upcoming CE incubator. These ideas are the results of a

seven-stage research process. To be brief, we have sacrificed nuance. In the upcoming weeks, full reports will be announced

in our newsletter, published

on our website, and posted on the EA Forum.

Cage-free in the Middle East: An organization focused on good-cop cage-free corporate campaigning in neglected countries in the Middle East (United Arab Emirates, Saudi Arabia, and Egypt).

Keel Bone Fractures: A charity working on how farmers can reduce the prevalence of keel bone fractures (KBF) in cage-free layer hens, ideally through outreach to certifiers to update their certification standards to include an outcome-based limit on KBF.

Fish Welfare East Asia - We continue to recommend an organization that works with farmers in neglected, high-priority countries in East Asia (Philippines, Taiwan, and Indo...

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: Forum update: User database, card view, and more (Jul 2024), published by Sarah Cheng on July 25, 2024 on The Effective Altruism Forum.

Highlights since our last feature update:

People directory

Google Doc import

Card view for the Frontpage

User profile updates

Updates to sequences

We've also hosted 3 events, significantly improved the site speed, and made many more improvements across the Forum.

As always, we'd love feedback on these changes.

People directory

We've built a filterable database where anyone can find Forum users they might want to hire, collaborate with, or read.

You can filter the database by role, organization, interests and more.

If you want people to easily find you, consider adding more information to your Forum profile.

Google Doc import

You can now import posts directly from Google Docs. Footnotes and images will be imported along with the text.

How it works:

Write and format your post in Google Docs

Share the Google doc with [email protected]

Copy the link to your doc, click "Import Google doc" on a new Forum post, and paste the link

For more details, check out Will Howard's quick take.

Card view for the Frontpage

Card view is a new way to view the Frontpage, allowing you to see post images and a preview of the content.

Try it out by changing to card view in the dropdown to the right of "Customize feed".

User profile updates

We've redesigned the Edit profile page, and moved "Display name" to this page (from Account settings). Feel free to update your display name to add a GWWC pledge diamond.

We've also re-enabled topic interests. This helps possible collaborators or hiring managers find you in our new People directory, as well as letting you make your profile a bit more information dense and personalized.

Updates to sequences

You can now get notified when posts are added to a sequence. We've also updated the sequence editor, including adding the ability to delete sequences.

Other updates

Forum performance is greatly improved

We've made several behind the scenes changes which have sped up the Forum loading times. This should make your experience of using the Forum a lot smoother.

Improved on-site audio experience

While the previous version was static, attached to the top of the post, the new audio player sticks to the bottom of the page while the reader scrolls through the article. Once you have clicked the speaker icon to open the audio player, you can click the play button next to any header to start playing the audio from there.

We've fixed our Twitter bot

Our Twitter bot tweets posts when they hit 40 karma. It's been out of commission for a while, but now it's back up and running! We encourage you to follow, share, and retweet posts that you think are valuable.

Updated UI for post stats

We've simplified the UI and added high level metrics to your post stats.

Before and after:

Self-service account deletion

There is now a section at the bottom of your account settings allowing you to permanently delete your personal data from the Forum.

Events since March

Draft Amnesty Week

We hosted a Draft Amnesty Week to encourage people to publish posts that had been languishing in draft or procrastinated states. We had around 50 posts published.

We also made some design changes to how we show events on the Forum, which will make future events more visible and navigable.

"Ways the world is getting better" banner

This banner was on the Forum for a week. Users could add emojis to the banner, which would show a piece of good news when someone hovered over them. When you clicked the emojis, you would be linked to an article explaining the good news.

AI Welfare Debate Week

Our first interactive debate week had over 500 Forum users voting on our interactive banner, around 30 posts, and much discussion in comments and quick takes. We received a lot of enco...

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: It's OK to kill and eat animals - but don't get caught slapping one., published by Denis on July 25, 2024 on The Effective Altruism Forum.

Very short post. This is not my area of expertise at all. But it seems like an opportunity.

The Olympics start this week. In the UK, the biggest Olympic story is not about any runner or swimmer or gymnast. It is about animal rights. But, as with most animal-rights stories which make the front-pages (bull-fighting, hunting), it misses the real problem, factory-farming.

The story: Apparently a famous Olympian equestrian has been forced to withdraw from the Olympics after footage emerged of her whipping a horse during training, 4 years ago. Cue the standard apologies, the "error of judgment" comment, the universal condemnation - and of course the video is shared with a warning that people might find this shocking.

I think it would be wonderful if someone with the right moral stature (which is not me, I'm not even a vegan ...) were to highlight the absurdity of so much moral outrage for an otherwise well-treated, well-fed horse who gets whipped on the leg one time, but no reaction to the billions of factory-farmed animals who suffer in cages for their entire lives before we kill them and eat them.

Maybe it would make people think again about factory-farming, or at least ask themselves if their views on animals were consistent.

I was reminded of the Tolstoy description of a lady who "faints when she sees a calf being killed, she is so kind hearted that she can't look at the blood, but enjoys serving the calf up with sauce."

My point with this post is just that if someone is in a position to express a public opinion on this, or write a letter to the editor, it might be an opportune moment given the size of the story right now.

Charlotte Dujardin out of Olympics: The video, the reaction and what happens now explained | Olympics News | Sky Sports

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