EA organizations frequently ask for people to run criticism by them ahead of time. I’ve been wary of the push for this norm. My big concerns were that orgs wouldn’t comment until a post was nearly done, and that it would take a lot of time. My recent post  mentioned a lot of people and organizations, so it seemed like useful data. I reached out to 12 email addresses, plus one person in FB DMs and one open call for information on a particular topic.  This doesn’t quite match what you see in the post because some people/orgs were used more than once, and other mentions were cut. The post was in a fairly crude state when I sent it out. Of those 14: 10 had replied by the start of next day. More than half of those replied within a few hours. I expect this was faster than usual because no one had more than a few paragraphs relevant to them or their org, but is still impressive. It’s hard to say how sending an early draft changed things. One person got some extra anxiety because their paragraph was full of TODOs (because it was positive and I hadn’t worked as hard fleshing out the positive mentions ahead of time). I could maybe have saved myself one stressful interaction if I’d realized I was going to cut an example ahead of time Only 80,000 Hours, Anima International, and GiveDirectly failed to respond before publication (7 days after I emailed them).  I didn’t keep as close track of changes, but at a minimum replies led to 2 examples being removed entirely, 2 clarifications and some additional information that made the post better. So overall I'm very glad I solicited comments, and found the process easier than expected. 

How useful is pre university student collations of research papers in biorisk? I've been working on some papers (for fun) collating research in the biosafety field, but obviously have no experience/degrees and it is secondary analysis- how useful would posting these 'rough' papers be helpful. They mainly focus on antibiotic resistance, biosafety and pandemic risk from gain of function research?

Do we know if @Paul_Christiano or other ex-lab people working on AI policy have non-disparagement agreements with OpenAI or other AI companies? I know Cullen doesn't, but I don't know about anybody else. I know NIST isn't a regulatory body, but it still seems like standards-setting should be done by people who have no unusual legal obligations. And of course, some other people are or will be working at regulatory bodies, which may have more teeth in the future. To be clear, I want to differentiate between Non-Disclosure Agreements, which are perfectly sane and reasonable in at least a limited form as a way to prevent leaking trade secrets, and non-disparagement agreements, which prevents you from saying bad things about past employers. The latter seems clearly bad to have for anybody in a position to affect policy. Doubly so if the existence of the non-disparagement agreement itself is secretive.

Having a baby and becoming a parent has had an incredible impact on me. Now more than ever, I feel more connected and concerned about the wellbeing of others. I feel as though my heart has literally grown. I wanted to share this as I expect there are many others who are questioning whether to have children -- perhaps due to concerns about it limiting their positive impact, among many others. But I'm just here to say it's been beautiful, and amazing, and I look forward to the day I get to talk with my son about giving back in a meaningful way.  

I wonder how the recent turn for the worse at OpenAI should make us feel about e.g. Anthropic and Conjecture and other organizations with a similar structure, or whether we should change our behaviour towards those orgs. * How much do we think that OpenAI's problems are idiosyncratic vs. structural? If e.g. Sam Altman is the problem, we can still feel good about peer organisations. If instead weighing investor concerns and safety concerns is the root of the problem, we should be worried about whether peer organizations are going to be pushed down the same path sooner or later. * Are there any concerns we have with OpenAI that we should be taking this opportunity to put to its peers as well? For example, have peers been publically asked if they use non-disparagement agreements? I can imagine a situation where another org has really just never thought to use them, and we can use this occasion to encourage them to turn that into a public commitment.

Besides Ilya Sutskever, is there any person not related to the EA community who quit or was fired from OpenAI for safety concerns?

I don't think CEA has a public theory of change, it just has a strategy. If I were to recreate its theory of change based on what I know of the org, it'd have three target groups: 1. Non-EAs 2. Organisers 3. Existing members of the community Per target group, I'd say it has the following main activities: * Targeting non-EAs, it does comms and education (the VP programme). * Targeting organisers, you have the work of the groups team. * Targeting existing members, you have the events team, the forum team, and community health.  Per target group, these activities are aiming for the following short-term outcomes: * Targeting non-EAs, it doesn't aim to raise awareness of EA, but instead, it aims to ensure people have an accurate understanding of what EA is. * Targeting organisers, it aims to improve their ability to organise. * Targeting existing members, it aims to improve information flow (through EAG(x) events, the forum, newsletters, etc.) and maintain a healthy culture (through community health work). If you're interested, you can see EA Netherland's theory of change here. 

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.

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.

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).

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.

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.

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.

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!

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.