Draft guidelines for new topic tags (feedback welcome) Topics (AKA wiki pages[1] or tags[2]) are used to organise Forum posts into useful groupings. They can be used to give readers context on a debate that happens only intermittently (see Time of Perils), collect news and events which might interest people in a certain region (see Greater New York City Area), collect the posts by an organisation, or, perhaps most importantly, collect all the posts on a particular subject (see Prediction Markets).  Any user can submit and begin using a topic. They can do this most easily by clicking “Add topic” on the topic line at the top of any post. However, before being permanently added to our list of topics, all topics are vetted by the Forum facilitation team. This quick take outlines some requirements and suggestions for new topics to make this more transparent. Similar, more polished, advice will soon be available on the 'add topic' page. Please give feedback if you disagree with any of these requirements.  When you add a new topic, ensure that: 1. The topic, or a very similar topic, does not already exist. If a very similar topic already exists, consider adding detail to that topic wiki page rather than creating a new topic.  2. You have used your topic to tag at least three posts by different authors (not including yourself). You will have to do this after creating the topic. The topic must describe a central theme in each post. If you cannot yet tag three relevant posts, the Forum probably doesn’t need this topic yet.  3. You’ve added at least a couple of sentences to define the term and explain how the topic tag should be used.    Not fulfilling these requirements is the most likely cause of a topic rejection. In particular, many topics are written with the aim of establishing a new term or idea, rather than collecting terms and ideas which already exist on the Forum. Other examples of rejected topics include: * Topic pages created for an individual. In certain cases, we permit these tags, for example, if the person is associated with a philosophy or set of ideas that is often discussed (see Peter Singer) and which can be clearly picked out by their name. However, in most cases, we don’t want tags for individuals because there would be far too many, and posts about individuals can generally be found through search without using tags. * Topics which are applicable to posts on the EA Forum, but which aren’t used by Forum users. For example, many posts could technically be described as “Risk Management”. However, EA forum users use other terms to refer to risk management content. 1. ^ Technically there can be a wiki page without a topic tag, i.e. a wiki page that cannot be applied to a post. However we don’t really use these, so in practice the terms are interchangeable. 2. ^ This term is used more informally. It is easier to say “I’m tagging this post” than “I’m topic-ing this post”

I spent way too much time organizing my thoughts on AI loss-of-control ("x-risk") debates without any feedback today, so I'm publishing perhaps one of my favorite snippets/threads: A lot of debates seem to boil down to under-acknowledged and poorly-framed disagreements about questions like “who bears the burden of proof.” For example, some skeptics say “extraordinary claims require extraordinary evidence” when dismissing claims that the risk is merely “above 1%”, whereas safetyists argue that having >99% confidence that things won’t go wrong is the “extraordinary claim that requires extraordinary evidence.”  I think that talking about “burdens” might be unproductive. Instead, it may be better to frame the question more like “what should we assume by default, in the absence of definitive ‘evidence’ or arguments, and why?” “Burden” language is super fuzzy (and seems a bit morally charged), whereas this framing at least forces people to acknowledge that some default assumptions are being made and consider why.  To address that framing, I think it’s better to ask/answer questions like “What reference class does ‘building AGI’ belong to, and what are the base rates of danger for that reference class?” This framing at least pushes people to make explicit claims about what reference class building AGI belongs to, which should make it clearer that it doesn’t belong in your “all technologies ever” reference class.  In my view, the "default" estimate should not be “roughly zero until proven otherwise,” especially given that there isn’t consensus among experts and the overarching narrative of “intelligence proved really powerful in humans, misalignment even among humans is quite common (and is already often observed in existing models), and we often don’t get technologies right on the first few tries.”

I am not under any non-disparagement obligations to OpenAI. It is important to me that people know this, so that they can trust any future policy analysis or opinions I offer. I have no further comments at this time.

This is a cold take that’s probably been said before, but I thought it bears repeating occasionally, if only for the reminder: The longtermist viewpoint has gotten a lot of criticism for prioritizing “vast hypothetical future populations” over the needs of "real people," alive today. The mistake, so the critique goes, is the result of replacing ethics with math, or utilitarianism, or something cold and rigid like that. And so it’s flawed because it lacks the love or duty or "ethics of care" or concern for justice that lead people to alternatives like mutual aid and political activism. My go-to reaction to this critique has become something like “well you don’t need to prioritize vast abstract future generations to care about pandemics or nuclear war, those are very real things that could, with non-trivial probability, face us in our lifetimes.” I think this response has taken hold in general among people who talk about X-risk. This probably makes sense for pragmatic reasons. It’s a very good rebuttal to the “cold and heartless utilitarianism/pascal's mugging” critique. But I think it unfortunately neglects the critical point that longtermism, when taken really seriously — at least the sort of longtermism that MacAskill writes about in WWOTF, or Joe Carlsmith writes about in his essays — is full of care and love and duty. Reading the thought experiment that opens the book about living every human life in sequential order reminded me of this. I wish there were more people responding to the “longtermism is cold and heartless” critique by making the case that no, longtermism at face value is worth preserving because it's the polar opposite of heartless. Caring about the world we leave for the real people, with emotions and needs and experiences as real as our own, who very well may inherit our world but who we’ll never meet, is an extraordinary act of empathy and compassion — one that’s way harder to access than the empathy and warmth we might feel for our neighbors by default. It’s the ultimate act of care. And it’s definitely concerned with justice. (I mean, you can also find longtermism worthy because of something something math and cold utilitarianism. That’s not out of the question. I just don’t think it’s the only way to reach that conclusion.)

We should expect that the incentives and culture for AI-focused companies to make them uniquely terrible for producing safe AGI.    From a “safety from catastrophic risk” perspective, I suspect an “AI-focused company” (e.g. Anthropic, OpenAI, Mistral) is abstractly pretty close to the worst possible organizational structure for getting us towards AGI. I have two distinct but related reasons: 1. Incentives 2. Culture From an incentives perspective, consider realistic alternative organizational structures to “AI-focused company” that nonetheless has enough firepower to host successful multibillion-dollar scientific/engineering projects: 1. As part of an intergovernmental effort (e.g. CERN’s Large Hadron Collider, the ISS) 2. As part of a governmental effort of a single country (e.g. Apollo Program, Manhattan Project, China’s Tiangong) 3. As part of a larger company (e.g. Google DeepMind, Meta AI) In each of those cases, I claim that there are stronger (though still not ideal) organizational incentives to slow down, pause/stop, or roll back deployment if there is sufficient evidence or reason to believe that further development can result in major catastrophe. In contrast, an AI-focused company has every incentive to go ahead on AI when the case for pausing is uncertain, and minimal incentive to stop or even take things slowly.  From a culture perspective, I claim that without knowing any details of the specific companies, you should expect AI-focused companies to be more likely than plausible contenders to have the following cultural elements: 1. Ideological AGI Vision AI-focused companies may have a large contingent of “true believers” who are ideologically motivated to make AGI at all costs and 2. No Pre-existing Safety Culture AI-focused companies may have minimal or no strong “safety” culture where people deeply understand, have experience in, and are motivated by a desire to avoid catastrophic outcomes.  The first one should be self-explanatory. The second one is a bit more complicated, but basically I think it’s hard to have a safety-focused culture just by “wanting it” hard enough in the abstract, or by talking a big game. Instead, institutions (relatively) have more of a safe & robust culture if they have previously suffered the (large) costs of not focusing enough on safety. For example, engineers who aren’t software engineers understand fairly deep down that their mistakes can kill people, and that their predecessors’ fuck-up have indeed killed people (think bridges collapsing, airplanes falling, medicines not working, etc). Software engineers rarely have such experience. Similarly, governmental institutions have institutional memories with the problems of major historical fuckups, in a way that new startups very much don’t.

[PHOTO] I sent 19 emails to politicians, had 4 meetings, and now I get emails like this. There is SO MUCH low hanging fruit in just doing this for 30 minutes a day (I would do it but my LTFF funding does not cover this). Someone should do this!

We’re very excited to announce the following speakers for EA Global: London 2024: * Rory Stewart (Former MP, Host of The Rest is Politics podcast and Senior Advisor to GiveDirectly) on obstacles and opportunities in making aid agencies more effective. * Mary Phuong (Research Scientist at DeepMind) on dangerous capability evaluations and responsible scaling. * Mahi Klosterhalfen (CEO of the Albert Schweitzer Foundation) on combining interventions for maximum impact in farmed animal welfare. Applications close 19 May. Apply here and find more details on our website, you can also email the EA Global team at hello@eaglobal.org if you have any questions.

Congratulations to the EA Project For Awesome 2024 team, who managed to raise over $100k for AMF, GiveDirectly and ProVeg International by submitting promotional/informational videos to the project. There's been an effort to raise money for effective charities via Project For Awesome since 2017, and it seems like a really productive effort every time. Thanks to all involved! 

FAQ: “Ways the world is getting better” banner The banner will only be visible on desktop. If you can't see it, try expanding your window. It'll be up for a week.  How do I use the banner? 1. Click on an empty space to add an emoji,  2. Choose your emoji,  3. Write a one-sentence description of the good news you want to share,  4. Link an article or forum post that gives more information.  If you’d like to delete your entry, click the cross that appears when you hover over it. It will be deleted for everyone. What kind of stuff should I write? Anything that qualifies as good news relevant to the world's most important problems.  For example, Ben West’s recent quick takes (1, 2, 3). Avoid posting partisan political news, but the passage of relevant bills and policies is on topic.  Will my entry be anonymous? All submissions are displayed without your Forum name, so they are ~anonymous to users, however, usual moderation norms still apply (additionally, we may remove duplicates or borderline trollish submissions. This is an experiment, so we reserve the right to moderate heavily if necessary). Ask any other questions you have in the comments below. Feel free to dm me with feedback or comments.  

This could be a long slog but I think it could be valuable to identify the top ~100 OS libraries and identify their level of resourcing to avoid future attacks like the XZ attack. In general, I think work on hardening systems is an underrated aspect of defending against future highly capable autonomous AI agents.

Common prevalence estimates are often wrong. Example: snakebites and my experience reading Long Covid literature. Both institutions like the WHO and academic literature appear to be incentivized to exaggerate. I think the Global Burden of Disease might be a more reliable source, but have not looked into it. I advise everyone using prevalence estimates to treat them with some skepticism and look up the source.

Introducing Ulysses*, a new app for grantseekers.    We (Austin Chen, Caleb Parikh, and I) built an app! You can test the app out if you’re writing a grant application! You can put in sections of your grant application** and the app will try to give constructive feedback about your applicants. Right now we're focused on the "Track Record" and "Project Goals" section of the application. (The main hope is to save back-and-forth-time between applicants and grantmakers by asking you questions that grantmakers might want to ask. Austin, Caleb, and I hacked together a quick app as a fun experiment in coworking and LLM apps. We wanted a short project that we could complete in ~a day. Working on it was really fun! We mostly did it for our own edification, but we’d love it if the product is actually useful for at least a few people in the community! As grantmakers in AI Safety, we’re often thinking about how LLMs will shape the future; the idea for this app came out of brainstorming, “How might we apply LLMs to our own work?”. We reflected on common pitfalls we see in grant applications, and I wrote a very rough checklist/rubric and graded some Manifund/synthetic applications against the rubric.  Caleb then generated a small number of few shot prompts by hand and then used LLMs to generate further prompts for different criteria (e.g., concreteness, honesty, and information on past projects) using a “meta-prompting” scheme. Austin set up a simple interface in Streamlit to let grantees paste in parts of their grant proposals. All of our code is open source on Github (but not open weight 😛).*** This is very much a prototype, and everything is very rough, but please let us know what you think! If there’s sufficient interest, we’d be excited about improving it (e.g., by adding other sections or putting more effort into prompt engineering). To be clear, the actual LLM feedback isn’t necessarily good or endorsed by us, especially at this very early stage. As usual, use your own best judgment before incorporating the feedback. *Credit to Saul for the name, who originally got the Ulysses S. Grant pun from Scott Alexander. ** Note: Our app will not be locally saving your data. We are using the OpenAI API for our LLM feedback. OpenAI says that it won’t use your data to train models, but you may still wish to be cautious with highly sensitive data anyway.  *** Linch led a discussion on the potential capabilities insights of our work, but we ultimately decided that it was asymmetrically good for safety; if you work on a capabilities team at a lab, we ask that you pay $20 to LTFF before you look at the repo.  

In this "quick take", I want to summarize some my idiosyncratic views on AI risk.  My goal here is to list just a few ideas that cause me to approach the subject differently from how I perceive most other EAs view the topic. These ideas largely push me in the direction of making me more optimistic about AI, and less likely to support heavy regulations on AI. (Note that I won't spend a lot of time justifying each of these views here. I'm mostly stating these points without lengthy justifications, in case anyone is curious. These ideas can perhaps inform why I spend significant amounts of my time pushing back against AI risk arguments. Not all of these ideas are rare, and some of them may indeed be popular among EAs.) 1. Skepticism of the treacherous turn: The treacherous turn is the idea that (1) at some point there will be a very smart unaligned AI, (2) when weak, this AI will pretend to be nice, but (3) when sufficiently strong, this AI will turn on humanity by taking over the world by surprise, and then (4) optimize the universe without constraint, which would be very bad for humans. By comparison, I find it more likely that no individual AI will ever be strong enough to take over the world, in the sense of overthrowing the world's existing institutions and governments by surprise. Instead, I broadly expect unaligned AIs will integrate into society and try to accomplish their goals by advocating for their legal rights, rather than trying to overthrow our institutions by force. Upon attaining legal personhood, unaligned AIs can utilize their legal rights to achieve their objectives, for example by getting a job and trading their labor for property, within the already-existing institutions. Because the world is not zero sum, and there are economic benefits to scale and specialization, this argument implies that unaligned AIs may well have a net-positive effect on humans, as they could trade with us, producing value in exchange for our own property and services. Note that my claim here is not that AIs will never become smarter than humans. One way of seeing how these two claims are distinguished is to compare my scenario to the case of genetically engineered humans. By assumption, if we genetically engineered humans, they would presumably eventually surpass ordinary humans in intelligence (along with social persuasion ability, and ability to deceive etc.). However, by itself, the fact that genetically engineered humans will become smarter than non-engineered humans does not imply that genetically engineered humans would try to overthrow the government. Instead, as in the case of AIs, I expect genetically engineered humans would largely try to work within existing institutions, rather than violently overthrow them. 2. AI alignment will probably be somewhat easy: The most direct and strongest current empirical evidence we have about the difficulty of AI alignment, in my view, comes from existing frontier LLMs, such as GPT-4. Having spent dozens of hours testing GPT-4's abilities and moral reasoning, I think the system is already substantially more law-abiding, thoughtful and ethical than a large fraction of humans. Most importantly, this ethical reasoning extends (in my experience) to highly unusual thought experiments that almost certainly did not appear in its training data, demonstrating a fair degree of ethical generalization, beyond mere memorization. It is conceivable that GPT-4's apparently ethical nature is fake. Perhaps GPT-4 is lying about its motives to me and in fact desires something completely different than what it professes to care about. Maybe GPT-4 merely "understands" or "predicts" human morality without actually "caring" about human morality. But while these scenarios are logically possible, they seem less plausible to me than the simple alternative explanation that alignment—like many other properties of ML models—generalizes well, in the natural way that you might similarly expect from a human. Of course, the fact that GPT-4 is easily alignable does not immediately imply that smarter-than-human AIs will be easy to align. However, I think this current evidence is still significant, and aligns well with prior theoretical arguments that alignment would be easy. In particular, I am persuaded by the argument that, because evaluation is usually easier than generation, it should be feasible to accurately evaluate whether a slightly-smarter-than-human AI is taking bad actions, allowing us to shape its rewards during training accordingly. After we've aligned a model that's merely slightly smarter than humans, we can use it to help us align even smarter AIs, and so on, plausibly implying that alignment will scale to indefinitely higher levels of intelligence, without necessarily breaking down at any physically realistic point. 3. The default social response to AI will likely be strong: One reason to support heavy regulations on AI right now is if you think the natural "default" social response to AI will lean too heavily on the side of laissez faire than optimal, i.e., by default, we will have too little regulation rather than too much. In this case, you could believe that, by advocating for regulations now, you're making it more likely that we regulate AI a bit more than we otherwise would have, pushing us closer to the optimal level of regulation. I'm quite skeptical of this argument because I think that the default response to AI (in the absence of intervention from the EA community) will already be quite strong. My view here is informed by the base rate of technologies being overregulated, which I think is quite high. In fact, it is difficult for me to name even a single technology that I think is currently clearly underregulated by society. By pushing for more regulation on AI, I think it's likely that we will overshoot and over-constrain AI relative to the optimal level. In other words, my personal bias is towards thinking that society will regulate technologies too heavily, rather than too loosely. And I don't see a strong reason to think that AI will be any different from this general historical pattern. This makes me hesitant to push for more regulation on AI, since on my view, the marginal impact of my advocacy would likely be to push us even further in the direction of "too much regulation", overshooting the optimal level by even more than what I'd expect in the absence of my advocacy. 4. I view unaligned AIs as having comparable moral value to humans: This idea was explored in one of my most recent posts. The basic idea is that, under various physicalist views of consciousness, you should expect AIs to be conscious, even if they do not share human preferences. Moreover, it seems likely that AIs — even ones that don't share human preferences — will be pretrained on human data, and therefore largely share our social and moral concepts. Since unaligned AIs will likely be both conscious and share human social and moral concepts, I don't see much reason to think of them as less "deserving" of life and liberty, from a cosmopolitan moral perspective. They will likely think similarly to the way we do across a variety of relevant axes, even if their neural structures are quite different from our own. As a consequence, I am pretty happy to incorporate unaligned AIs into the legal system and grant them some control of the future, just as I'd be happy to grant some control of the future to human children, even if they don't share my exact values. Put another way, I view (what I perceive as) the EA attempt to privilege "human values" over "AI values" as being largely arbitrary and baseless, from an impartial moral perspective. There are many humans whose values I vehemently disagree with, but I nonetheless respect their autonomy, and do not wish to deny these humans their legal rights. Likewise, even if I strongly disagreed with the values of an advanced AI, I would still see value in their preferences being satisfied for their own sake, and I would try to respect the AI's autonomy and legal rights. I don't have a lot of faith in the inherent kindness of human nature relative to a "default unaligned" AI alternative. 5. I'm not fully committed to longtermism: I think AI has an enormous potential to benefit the lives of people who currently exist. I predict that AIs can eventually substitute for human researchers, and thereby accelerate technological progress, including in medicine. In combination with my other beliefs (such as my belief that AI alignment will probably be somewhat easy), this view leads me to think that AI development will likely be net-positive for people who exist at the time of alignment. In other words, if we allow AI development, it is likely that we can use AI to reduce human mortality, and dramatically raise human well-being for the people who already exist. I think these benefits are large and important, and commensurate with the downside potential of existential risks. While a fully committed strong longtermist might scoff at the idea that curing aging might be important — as it would largely only have short-term effects, rather than long-term effects that reverberate for billions of years — by contrast, I think it's really important to try to improve the lives of people who currently exist. Many people view this perspective as a form of moral partiality that we should discard for being arbitrary. However, I think morality is itself arbitrary: it can be anything we want it to be. And I choose to value currently existing humans, to a substantial (though not overwhelming) degree. This doesn't mean I'm a fully committed near-termist. I sympathize with many of the intuitions behind longtermism. For example, if curing aging required raising the probability of human extinction by 40 percentage points, or something like that, I don't think I'd do it. But in more realistic scenarios that we are likely to actually encounter, I think it's plausibly a lot better to accelerate AI, rather than delay AI, on current margins. This view simply makes sense to me given the enormously positive effects I expect AI will likely have on the people I currently know and love, if we allow development to continue.

Animal Justice Appreciation Note Animal Justice et al. v A.G of Ontario 2024 was recently decided and struck down large portions of Ontario's ag-gag law. A blog post is here. The suit was partially funded by ACE, which presumably means that many of the people reading this deserve partial credit for donating to support it. Thanks to Animal Justice (Andrea Gonsalves, Fredrick Schumann, Kaitlyn Mitchell, Scott Tinney), co-applicants Jessica Scott-Reid and Louise Jorgensen, and everyone who supported this work!

I worked at OpenAI for three years, from 2021-2024 on the Alignment team, which eventually became the Superalignment team. I worked on scalable oversight, part of the team developing critiques as a technique for using language models to spot mistakes in other language models. I then worked to refine an idea from Nick Cammarata into a method for using language model to generate explanations for features in language models. I was then promoted to managing a team of 4 people which worked on trying to understand language model features in context, leading to the release of an open source "transformer debugger" tool. I resigned from OpenAI on February 15, 2024.

First in-ovo sexing in the US Egg Innovations announced that they are "on track to adopt the technology in early 2025." Approximately 300 million male chicks are ground up alive in the US each year (since only female chicks are valuable) and in-ovo sexing would prevent this.  UEP originally promised to eliminate male chick culling by 2020; needless to say, they didn't keep that commitment. But better late than never!  Congrats to everyone working on this, including @Robert - Innovate Animal Ag, who founded an organization devoted to pushing this technology.[1] 1. ^ Egg Innovations says they can't disclose details about who they are working with for NDA reasons; if anyone has more information about who deserves credit for this, please comment!

I think some of the AI safety policy community has over-indexed on the visual model of the "Overton Window" and under-indexed on alternatives like the "ratchet effect," "poisoning the well," "clown attacks," and other models where proposing radical changes can make you, your allies, and your ideas look unreasonable. I'm not familiar with a lot of systematic empirical evidence on either side, but it seems to me like the more effective actors in the DC establishment overall are much more in the habit of looking for small wins that are both good in themselves and shrink the size of the ask for their ideal policy than of pushing for their ideal vision and then making concessions. Possibly an ideal ecosystem has both strategies, but it seems possible that at least some versions of "Overton Window-moving" strategies executed in practice have larger negative effects via associating their "side" with unreasonable-sounding ideas in the minds of very bandwidth-constrained policymakers, who strongly lean on signals of credibility and consensus when quickly evaluating policy options, than the positive effects of increasing the odds of ideal policy and improving the framing for non-ideal but pretty good policies. In theory, the Overton Window model is just a description of what ideas are taken seriously, so it can indeed accommodate backfire effects where you argue for an idea "outside the window" and this actually makes the window narrower. But I think the visual imagery of "windows" actually struggles to accommodate this -- when was the last time you tried to open a window and accidentally closed it instead? -- and as a result, people who rely on this model are more likely to underrate these kinds of consequences. Would be interested in empirical evidence on this question (ideally actual studies from psych, political science, sociology, econ, etc literatures, rather than specific case studies due to reference class tennis type issues).

Please people, do not treat Richard Hannania as some sort of worthy figure who is a friend of EA. He was a Nazi, and whilst he claims he moderated his views, he is still very racist as far as I can tell. Hannania called for trying to get rid of all non-white immigrants in the US, and the sterilization of everyone with an IQ under 90 indulged in antisemitic attacks on the allegedly Jewish elite, and even post his reform was writing about the need for the state to harass and imprison Black people specifically ('a revolution in our culture or form of government. We need more policing, incarceration, and surveillance of black people' https://en.wikipedia.org/wiki/Richard_Hanania).  Yet in the face of this, and after he made an incredibly grudging apology about his most extreme stuff (after journalists dug it up), he's been invited to Manifiold's events and put on Richard Yetter Chappel's blogroll.  DO NOT DO THIS. If you want people to distinguish benign transhumanism (which I agree is a real thing*) from the racist history of eugenics, do not fail to shun actual racists and Nazis. Likewise, if you want to promote "decoupling" factual beliefs from policy recommendations, which can be useful, do not duck and dive around the fact that virtually every major promoter of scientific racism ever, including allegedly mainstream figures like Jensen, worked with or published with actual literal Nazis (https://www.splcenter.org/fighting-hate/extremist-files/individual/arthur-jensen).  I love most of the people I have met through EA, and I know that-despite what some people say on twitter- we are not actually a secret crypto-fascist movement (nor is longtermism specifically, which whether you like it or not, is mostly about what its EA proponents say it is about.) But there is in my view a disturbing degree of tolerance for this stuff in the community, mostly centered around the Bay specifically. And to be clear I am complaining about tolerance for people with far-right and fascist ("reactionary" or whatever) political views, not people with any particular personal opinion on the genetics of intelligence. A desire for authoritarian government enforcing the "natural" racial hierarchy does not become okay, just because you met the person with the desire at a house party and they seemed kind of normal and chill or super-smart and nerdy.  I usually take a way more measured tone on the forum than this, but here I think real information is given by getting shouty.  *Anyone who thinks it is automatically far-right to think about any kind of genetic enhancement at all should go read some Culture novels, and note the implied politics (or indeed, look up the author's actual die-hard libertarian socialist views.) I am not claiming that far-left politics is innocent, just that it is not racist. 

You can now import posts directly from Google docs Plus, internal links to headers[1] will now be mapped over correctly. To import a doc, make sure it is public or shared with "eaforum.posts@gmail.com"[2], then use the widget on the new/edit post page: Importing a doc will create a new (permanently saved) version of the post, but will not publish it, so it's safe to import updates into posts that are already published. You will need to click the "Publish Changes" button to update the live post. Everything that previously worked on copy-paste[3] will also work when importing, with the addition of internal links to headers (which only work when importing). There are still a few things that are known not to work: * Nested bullet points (these are working now) * Cropped images get uncropped * Bullet points in footnotes (these will become separate un-bulleted lines) * Blockquotes (there isn't a direct analog of this in Google docs unfortunately) There might be other issues that we don't know about. Please report any bugs or give any other feedback by replying to this quick take, you can also contact us in the usual ways. Appendix: Version history There are some minor improvements to the version history editor[4] that come along with this update: * You can load a version into the post editor without updating the live post, previously you could only hard-restore versions * The version that is live[5] on the post is shown in bold Here's what it would look like just after you import a Google doc, but before you publish the changes. Note that the latest version isn't bold, indicating that it is not showing publicly: 1. ^ Previously the link would take you back to the original doc, now it will take you to the header within the Forum post as you would expect. Internal links to bookmarks (where you link to a specific text selection) are also partially supported, although the link will only go to the paragraph the text selection is in 2. ^ Sharing with this email address means that anyone can access the contents of your doc if they have the url, because they could go to the new post page and import it. It does mean they can't access the comments at least 3. ^ I'm not sure how widespread this knowledge is, but previously the best way to copy from a Google doc was to first "Publish to the web" and then copy-paste from this published version. In particular this handles footnotes and tables, whereas pasting directly from a regular doc doesn't. The new importing feature should be equal to this publish-to-web copy-pasting, so will handle footnotes, tables, images etc. And then it additionally supports internal links 4. ^ Accessed via the "Version history" button in the post editor 5. ^ For most intents and purposes you can think of "live" as meaning "showing publicly". There is a bit of a sharp corner in this definition, in that the post as a whole can still be a draft. To spell this out: There can be many different versions of a post body, only one of these is attached to the post, this is the "live" version. This live version is what shows on the non-editing view of the post. Independently of this, the post as a whole can be a draft or published.

Why are April Fools jokes still on the front page? On April 1st, you expect to see April Fools' posts and know you have to be extra cautious when reading strange things online. However, April 1st was 13 days ago and there are still two posts that are April Fools posts on the front page. I think it should be clarified that they are April Fools jokes so people can differentiate EA weird stuff from EA weird stuff that's a joke more easily. Sure, if you check the details you'll see that things don't add up, but we all know most people just read the title or first few paragraphs.

Marcus Daniell appreciation note @Marcus Daniell, cofounder of High Impact Athletes, came back from knee surgery and is donating half of his prize money this year. He projects raising $100,000. Through a partnership with Momentum, people can pledge to donate for each point he gets; he has raised $28,000 through this so far. It's cool to see this, and I'm wishing him luck for his final year of professional play!

David Nash's Monthly Overload of Effective Altruism seems highly underrated, and you should most probably give it a follow. I don't think any other newsletter captures and highlights EA's cause-neutral impartial beneficence better than the Monthly Overload of EA. For example, this month's newsletter has updates about Conferences, Virtual Events, Meta-EA, Effective Giving, Global Health and Development, Careers, Animal Welfare, Organization updates, Grants, Biosecurity, Emissions & CO2 Removal, Environment, AI Safety, AI Governance, AI in China, Improving Institutions, Progress, Innovation & Metascience, Longtermism, Forecasting, Miscellaneous causes and links, Stories & EA Around the World, Good News, and more. Compiling all this must be hard work! Until September 2022, the monthly overloads were also posted on the Forum and received higher engagement than the Substack. I find the posts super informative, so I am giving the newsletter a shout-out and putting it back on everyone's radar!