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: How to have high impact conversations, published by Michelle Hutchinson on August 14, 2024 on The Effective Altruism Forum. I run the 80,000 Hours one-on-one team. We speak to about 1.5k people a year about their careers. Our internal term for these people, which I'll use throughout this post, is 'advisees'. One of the biggest ways we help people is by making introductions to people working in high impact fields (we refer to them internally as 'specialists'). Below is the guidance we give specialists on how to make those introductions worthwhile. Background One of the core things we're trying to figure out on an advisee call is whether there are other people we know about who could be useful for the person to chat to. People have reported these kinds of introductions to have been surprisingly useful in the past. Sometimes it's very clear what the value of an introduction can be, such as putting a hiring manager in touch with someone who has the profile they're looking for. But there are quite a few other things that have turned out to be useful over the years, such as: Specialists giving a sense of their day to day in a particular job and thereby give someone a sense of whether they're likely to be suited to that role or not Specialists talking about their background and how they got into a job, to give a sense of how to get into their role Social proof - putting a face and voice to a particular sector, and making an advisee feel more like they too could get into that sector Particular esoteric knowledge about a field or path which it's hard to find as an outsider to it. I do really want to recommend that people thinking about their careers seek out more conversations with people working in fields they're interested in or who have thought through similar kinds of decisions. It's also worth taking time to prep in order to get additional value from them. It's nerve wracking to ask for conversations like this ( some advice from Michael Aird on how to do this). But I think people are often happy to be asked for their advice. In fact, people working in a field may themselves feel reticent to offer calls to talk about how they got to where they are. To them, it's far more salient how far they have to go rather than how much they've already achieved. I thought seeing our list of tips might give people on either side of this a little more confidence to initiate these conversations. The below are the suggestions we make to the experts in the field to whom we make introductions. They were in large part written by Alex Lawsen and Anemone Franz, with input from some specialists. The system is now run by Zac Richardson. Tips on how to make the conversations you have more useful It's sometimes useful to ask the advisee to come to the call with a list of questions or topic prepared. If you'd like us to ask all advisees to do this, just let us know! If you're happy to take a bit more ownership of part of the call, it can be helpful to ask the person you're speaking to what they are most hoping to get out of the conversation (we typically do this on advising calls). We will have given both of you some context on each other in the introductory email, but it can still be worth giving a brief personal introduction, for example explaining what you're working on at the moment. Starting the call by asking some open-ended questions of the person you're advising can be helpful to get a better sense of them. This could be about their current career plans, or something broader, like what they have been thinking about recently. It can be helpful to set clear expectations at the beginning of the call about the scope of your expertise and the topics you feel comfortable discussing, especially if you work in a complicated field with lots of internal disagreement. Not giving a take on a particular ...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: An anti-inductive sequence, published by Viliam on August 14, 2024 on LessWrong. I was thinking about what would it mean for a sequence of bits to be "anti-inductive". It probably is a concept that is already known (as a rule of thumb, if I can think about it, someone probably already wrote a paper on it 50 years ago), but I haven't heard about it. * Some sequences are predictable and can be compressed. These two concepts are deeply related, because if you can successfully predict the next part of the sequence, you don't need to actually write it down; hence compression. A completely random sequence of bits cannot be compressed or predicted. There is a simple mathematical proof that some sequences cannot be compressed, although it doesn't say which ones. For any natural number N, there are more sequences of size exactly N, than sequences of size smaller than N. Therefore no program can generate a unique sequence shorter than N for any input sequence of size N. * Things get more complicated if we consider the caveat that although random sequences in general cannot be compressed, true randomness means that sometimes we accidentally get a sequence that can be compressed -- for example, with probability 1/2ᴺ we get a sequence of N zeroes, and it would sound silly to argue that we can't compress that! The solution to this paradox is that if we decide to compress only some selected sequences, then we need to add an extra bit of information specifying whether this sequence was compressed or not. Otherwise, if we see a sequence of bits saying (in binary) "a sequence of thousand zeroes", we wouldn't know whether the intended value is this very sequence of bits taken literally, or the sequence of thousand zeroes. One bit doesn't seem like much, but actually most sequences cannot be compressed, so the cost of adding one extra bit to each of them outweighs the occasional space we would save by compressing the ones we can. But still, if I needed a random sequence of bits to use e.g. as a password for something important... and by a miracle I generated a sequence of N zeroes... I would probably feel quite uncomfortable to use it, and would simply generate a new one. Is this a good security practice, or not? Because from certain perspective, by removing the "insufficiently random" sequences from the pool of possible choices, I am reducing the size of the pool, which... kinda makes it easier to guess the password? Something similar actually happened to me once. I used a mobile application that asked me to create a 4-digit PIN. So I typed 4 random digits, and the application said: "nope, you cannot use the same digit multiple times in the PIN, that is not secure". So I typed 4 random digits again, and the application said: "nope, you cannot use an ascending sequence of digits, that is not secure". So I typed 4 random digits yet again, and this time the application was finally satisfied. But it felt funny that the more "security checks" the application uses, the more limited is the choice of possible PINs. There are only 10000 possible combinations of 4 digits to start with; I wonder how far an overzealous security department could reduce that number. In a hypothetical extreme case, we would be left with only one possible choice of PIN -- certainly the most secure one that no one could possibly guess! The holy grail of information security. * Okay, back to the sequences of bits. Imagine that we are trying to create not just any random sequence, but the single most random, most unpredictable sequence ever! Suppose the length of the sequence is not specified in advance, so we just keep generating bits one by one, for as long as necessary. What we could do is create a predictor -- an algorithm that looks at the previously generated bits, tries to find all possible patterns in them and pr...

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: Funding for work that builds capacity to address risks from transformative AI, published by GCR Capacity Building team (Open Phil) on August 14, 2024 on The Effective Altruism Forum. Post authors: Eli Rose, Asya Bergal Posting in our capacities as members of Open Philanthropy's Global Catastrophic Risks Capacity Building team. This program, together with our separate program for programs and events on global catastrophic risk, effective altruism, and other topics, has replaced our 2021 request for proposals for outreach projects. If you have a project which was in-scope for that program but isn't for either of these, you can apply to our team's general application instead. We think it's possible that the coming decades will see "transformative" progress in artificial intelligence, i.e., progress that leads to changes in human civilization at least as large as those brought on by the Agricultural and Industrial Revolutions. It is currently unclear to us whether these changes will go well or poorly, and we think that people today can do meaningful work to increase the likelihood of positive outcomes. To that end, we're interested in funding projects that: Help new talent get into work focused on addressing risks from transformative AI. Including people from academic or professional fields outside computer science or machine learning. Support existing talent in this field (e.g. via events that help build professional networks). Contribute to the discourse about transformative AI and its possible effects, positive and negative. We refer to this category of work as "capacity-building", in the sense of "building society's capacity" to navigate these risks. Types of work we've historically funded include training and mentorship programs, events, groups, and resources (e.g., blog posts, magazines, podcasts, videos), but we are interested in receiving applications for any capacity-building projects aimed at risks from advanced AI. This includes applications from both organizations and individuals, and includes both full-time and part-time projects. Apply for funding here. Applications are open until further notice and will be assessed on a rolling basis. We're interested in funding work to build capacity in a number of different fields which we think may be important for navigating transformative AI, including (but very much not limited to) technical alignment research, model evaluation and forecasting, AI governance and policy, information security, and research on the economics of AI. This program is not primarily intended to fund direct work in these fields, though we expect many grants to have both direct and capacity-building components - see below for more discussion. Categories of work we're interested in Training and mentorship programs These are programs that teach relevant skills or understanding, offer mentorship or professional connections for those new to a field, or provide career opportunities. These could take the form of fellowships, internships, residencies, visitor programs, online or in-person courses, bootcamps, etc. Some examples of training and mentorship programs we've funded in the past: BlueDot's online courses on technical AI safety and AI governance. MATS's in-person research and educational seminar programs in Berkeley, California. ML4Good's in-person AI safety bootcamps in Europe. We've previously funded a number of such programs in technical alignment research, and we're excited to fund new programs in this area. But we think other relevant areas may be relatively neglected - for instance, programs focusing on compute governance or on information security for frontier AI models. For illustration, here are some (hypothetical) examples of programs we could be interested in funding: A summer research fellowship for individuals with technical backgr...

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: Superintelligent AI is possible in the 2020s, published by HunterJay on August 14, 2024 on LessWrong. Back in June 2023, Soroush Pour and I discussed AI timelines on his podcast, The AGI Show. The biggest difference between us was that I think "machines more intelligent than people are likely to be developed within a few years", and he thinks that it's unlikely to happen for at least a few decades.[1] We haven't really resolved our disagreement on this prediction in the year since, so I thought I would write up my main reasons for thinking we're so close to superintelligence, and why the various arguments made by Soroush (and separately by Arvind Narayanan and Sayash Kapoor) aren't persuasive to me. Part 1 - Why I Think We Are Close Empirically You can pick pretty much any trend relating to AI & computation, and it looks like this:[2] We keep coming up with new benchmarks, and they keep getting saturated. While there are still some notable holdouts such as ARC-AGI, SWE-Bench, and GPQA, previous holdouts like MATH also looked like this until they were solved by newer models. If these trends continue, it's hard to imagine things that AI won't be able to do in a few years time[3], unless they are bottlenecked by regulation (like being a doctor), or by robot hardware limitations (like being a professional football player)[4]. Practically The empirical trends are the result of several different factors; changes in network architecture, choice of hyperparameters, optimizers, training regimes, synthetic data creation, and data cleaning & selection. There are also many ideas in the space that have not been tried at scale yet. Hardware itself is also improving -- chips continue to double in price performance every 2-3 years, and training clusters are scaling up massively. It's entirely possible that some of these trends slow down -- we might not have another transformers-level architecture advance this decade, for instance -- but the fact that there are many different ways to continue improving AI for the foreseeable future makes me think that it is unlikely for progress to slow significantly. If we run out of text data, we can use videos. If we run out of that, we can generate synthetic data. If synthetic data doesn't generalise, we can get more efficient with what we have through better optimisers and training schedules. If that doesn't work, we can find architectures which learn the patterns more easily, and so on. In reality, all of these will be done at the same time and pretty soon (arguably already) the AIs themselves will be doing a significant share of the research and engineering needed to find and test new ideas[5]. This makes me think progress will accelerate rather than slow. Theoretically Humans are an existence proof of general intelligence, and since human cognition is itself just computation[6], there is no physical law stopping us from building another general intelligence (in silicon) given enough time and resources[7]. We can use the human brain as an upper bound for the amount of computation needed to get AGI (i.e. we know it can be done with the amount of computation done in the brain, but it might be possible with less)[8]. We think human brains do an equivalent of between 10^12 and 10^28 FLOP[9] per second [a hilariously wide range]. Supercomputers today can do 10^18. The physical, theoretical limit seems to be approximately 10^48 FLOP per second per kilogram. We can also reason that humans are a lower bound for the compute efficiency of the AGI (i.e. we know that with this amount of compute, we can get human-level intelligence, but it might be possible to do it with less)[10]. If humans are more efficient than current AI systems per unit of compute, then we know that more algorithmic progress must be possible as well. In other words, there seems to be...

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: Announcing the $200k EA Community Choice, published by Austin on August 14, 2024 on The Effective Altruism Forum. Manifund is hosting a $200k funding round for EA community projects, where the grant decisions are made by you. You can direct $100-$800 of funding towards the projects that have helped with your personal journey as an EA. Your choices then decide how $100k in matching will be allocated, via quadratic funding! Sign up here to get notified when the projects are live, or read on to learn more! Timeline Phase 1: Project registrations open (Aug 13) Project organizers can now create a page for their projects here, to raise funding as part of this EA Community Choice round. Community members can sign up for updates here, or recommend projects to sign up here. Phase 2: Community members receive funds (Aug 20) We'll give everyone $100 to donate; more if you've been active in the EA community. Fill out a 2-minute form to claim your $100, plus bonuses for: Donor: $100 for taking the GWWC 10% Pledge Organizer: $100 for organizing any EA group Scholar: $100 for having 100 or more karma on the EA Forum Volunteer: $100 for volunteering at an EAG(x), Future Forum, or Manifest Worker: $100 for working fulltime at an EA org, or full-time on an EA grant Senior: $100 for having done any of the above prior to 2022 Insider: $100 if you had a Manifund account before August 2024 You can then donate your money to any project in the Community Choice round! You can also leave comments about any specific project. This is a great way to share your experiences with the project organizer, or the rest of the EA community. Funds in Phase 2 will be capped at $100k, first-come-first-served. Phase 3: Funds matched and sent to projects (Sep 1) Projects and donations will be locked in at the end of August. Then, all money donated will be matched against a $100k quadratic funding pool. Unlike a standard 1:1 match, quadratic funding rewards a project with lots of small donors more than a project with few big donors. The broader the support, the bigger the match! Specifically, the match is proportional the square of the sum of square roots of individual donations. A toy example: Learn more about principles behind quadratic funding by reading Vitalik Buterin's explainer, watching this video, or playing with this simulator. What is an EA community project? We don't have a strict definition, but roughly any project which helps you: learn about EA, connect with the EA community, or grow your impact in the world. We're casting a wide net; projects do not have to explicitly identify as EA to qualify (though, we also endorse being proud of being EA). If you're not sure if you count, just apply! Examples of projects we'd love to fund: Community groups Regional groups like EA Philippines Cause-specific groups like Hive University groups like EA Tufts Physical spaces Coworking spaces like Lighthaven, Epistea, and AI Safety Serbia Hub Housing like CEEALAR and Berkeley REACH Events Conferences like Manifest, EAGx, LessOnline, and AI, Animals, and Digital Minds Extended gatherings like Manifest Summer Camp or Prague Fall Season Recurring meetups like local groups or online EA coworking Tournaments like Metaculus Tournaments or The Estimation Game Essay competitions like EA Criticism & Red Teaming Contest Software Tools like Squiggle, Carlo, Fatebook, or Guesstimate Visualizations like AI Digest Datasets like Donations List Website Educational programs Incubators like AI Safety Camp and Apart Hackathons Course materials like AI Safety Fundamentals Information resources Websites like AISafety.com Youtube channels like Rational Animations, Rob Miles, and A Happier World Podcasts like The 80k Podcast, The Dwarkesh Podcast, and The Inside View FAQ What is Manifund? Manifund is a platform for funding impactful pr...

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: Fields that I reference when thinking about AI takeover prevention, published by Buck Shlegeris on August 13, 2024 on The AI Alignment Forum. Is AI takeover like a nuclear meltdown? A coup? A plane crash? My day job is thinking about safety measures that aim to reduce catastrophic risks from AI (especially risks from egregious misalignment). The two main themes of this work are the design of such measures (what's the space of techniques we might expect to be affordable and effective) and their evaluation (how do we decide which safety measures to implement, and whether a set of measures is sufficiently robust). I focus especially on AI control, where we assume our models are trying to subvert our safety measures and aspire to find measures that are robust anyway. Like other AI safety researchers, I often draw inspiration from other fields that contain potential analogies. Here are some of those fields, my opinions on their strengths and weaknesses as analogies, and some of my favorite resources on them. Robustness to insider threats The single field that seems most closely analogous to AI control is reducing vulnerability to threats resulting from insiders, especially threats that arise from access to IT systems. That is, you have a bunch of employees who need to have access to various IT systems in order to do their jobs, and you're worried that some of these employees will abuse their access in order to accomplish some nefarious purpose. In a bank, you might worry about your tellers withdrawing money from customer accounts inappropriately. In a software company, you might worry about your employees sabotaging your code out of spite, violating user privacy for fun, or stealing your data to give it to a competitor. This is quite similar to the problem faced by AI developers when they're trying to control AIs (especially in internal deployments, which I argue is important here): All of the access the AI has is access the developer decided to give it; the developer faces a constant tradeoff between the productivity of the AI (which is maximized by being permissive) and the danger it poses (which is minimized by being restrictive). And obviously, because it's an AI it only has access to resources that are connected to the computer network where it's running, so we can restrict our attention to the IT resources. It's easy for the developer to constantly surveil the AI, which substantially reduces the space of strategies the AI can take. The developer has a lot of control over workflows, and is able to choose them to make it harder for the AI to subtly cause problems. For example, they can restrict affordances in ways that make it harder for AIs to cause problems without being noticed. An example of a similar technique in the insider threat prevention case is the use of administrative APIs instead of direct access to a system, as described in "Building Secure and Reliable Systems" chapter 5. That is: instead of having your engineers resolve issues by SSHing into production machines (which implicitly grants them very broad access that can lead to problems, intentional or accidental), you design an administrative API which allows the engineers to access just the affordances that usually suffice for them to do their job. Of course, they'll sometimes need to take actions that aren't available in the administrative API; this is called a breakglass mechanism. But because breakglass actions are rare, it's affordable to apply more scrutiny to them: you might use multi-party authorization, requiring another engineer to sign off on the commands before they take effect, or you might do auditing (e.g. apparently some SRE teams review all the breakglass actions at a weekly team meeting, which allows them to spot irregularities and discourage gratuitous use of the mechanism). Similar t...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: New Report: "Who's Selling You Frankenchickens?", published by aaronparr on August 13, 2024 on The Effective Altruism Forum. The Humane League UK have released our State of the Chicken Industry report today! Read it here. What is it? The State of the Chicken Industry report titled 'Who's selling you Frankenchickens?' argues that minimum welfare standards should be lifted across the board by supermarkets by signing up to the Better Chicken Commitment (BCC), particularly as welfare labels can be confusing and misleading. What is in it? It contains a summary of the industry as it stands, problems/solutions facing the BCC, information on THL UK's judicial review, public opinion polling on supermarket labels, and opinions concerning the chicken industry from experts, corporates, and groups from across different sectors. What can you use it for? This report is designed to highlight where businesses stand on implementing the Better Chicken Commitment in the UK. It can be used as a resource for campaigners and animal advocates to better understand the current landscape of the chicken industry as well as for businesses to recognise why standards need to improve. How to take action Please share the report amongst your networks, on social media, and within your newsletter. Supporters can also use our action page to target their own supermarkets. Simply click here, choose where you shop, find out whether your supermarket is selling Frankenchickens, and take action demanding an end to the sale of this suffering breed of animal. _____________________ Thank you for your support and please feel free to contact me with any questions. 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: Ten arguments that AI is an existential risk, published by KatjaGrace on August 13, 2024 on LessWrong. This is a snapshot of a new page on the AI Impacts Wiki. We've made a list of arguments[1] that AI poses an existential risk to humanity. We'd love to hear how you feel about them in the comments and polls. Competent non-aligned agents Summary: 1. Humans will build AI systems that are 'agents', i.e. they will autonomously pursue goals 2. Humans won't figure out how to make systems with goals that are compatible with human welfare and realizing human values 3. Such systems will be built or selected to be highly competent, and so gain the power to achieve their goals 4. Thus the future will be primarily controlled by AIs, who will direct it in ways that are at odds with long-run human welfare or the realization of human values Selected counterarguments: It is unclear that AI will tend to have goals that are bad for humans There are many forms of power. It is unclear that a competence advantage will ultimately trump all others in time This argument also appears to apply to human groups such as corporations, so we need an explanation of why those are not an existential risk People who have favorably discussed[2] this argument (specific quotes here): Paul Christiano (2021), Ajeya Cotra (2023), Eliezer Yudkowsky (2024), Nick Bostrom (2014[3]). See also: Full wiki page on the competent non-aligned agents argument Second species argument Summary: 1. Human dominance over other animal species is primarily due to humans having superior cognitive and coordination abilities 2. Therefore if another 'species' appears with abilities superior to those of humans, that species will become dominant over humans in the same way 3. AI will essentially be a 'species' with superior abilities to humans 4. Therefore AI will dominate humans Selected counterarguments: Human dominance over other species is plausibly not due to the cognitive abilities of individual humans, but rather because of human ability to communicate and store information through culture and artifacts Intelligence in animals doesn't appear to generally relate to dominance. For instance, elephants are much more intelligent than beetles, and it is not clear that elephants have dominated beetles Differences in capabilities don't necessarily lead to extinction. In the modern world, more powerful countries arguably control less powerful countries, but they do not wipe them out and most colonized countries have eventually gained independence People who have favorably discussed this argument (specific quotes here): Joe Carlsmith (2024), Richard Ngo (2020), Stuart Russell (2020[4]), Nick Bostrom (2015). See also: Full wiki page on the second species argument Loss of control via inferiority Summary: 1. AI systems will become much more competent than humans at decision-making 2. Thus most decisions will probably be allocated to AI systems 3. If AI systems make most decisions, humans will lose control of the future 4. If humans have no control of the future, the future will probably be bad for humans Selected counterarguments: Humans do not generally seem to become disempowered by possession of software that is far superior to them, even if it makes many 'decisions' in the process of carrying out their will In the same way that humans avoid being overpowered by companies, even though companies are more competent than individual humans, humans can track AI trustworthiness and have AI systems compete for them as users. This might substantially mitigate untrustworthy AI behavior People who have favorably discussed this argument (specific quotes here): Paul Christiano (2014), Ajeya Cotra (2023), Richard Ngo (2024). See also: Full wiki page on loss of control via inferiority Loss of control via speed Summary: 1. Advances in AI will produce...

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: Presenting five new effective giving initiatives, published by CE on August 13, 2024 on The Effective Altruism Forum. Introduction In May 2024, Ambitious Impact (AIM) ran a program to incubate new effective giving initiatives (EGIs) in partnership with Giving What We Can. In short, EGIs focus on raising awareness and funneling public and philanthropic donations to the most cost-effective charities worldwide. In the last few years, several Effective Giving Initiatives, such as Doneer Effectief, Effektiv Spenden, and Giving What We Can, have moved millions in funding to the best charities globally. The success of these and other similar organizations suggests that further initiatives in this space could be highly beneficial, given that many highly effective charities are bottlenecked by access to funding. This article introduces five new effective giving initiatives incubated through the program we ran earlier this year in their own words. It summarizes their country of operation, near-term plans, targets, and any room for additional seed funding.[1] Organization Summaries Ellis Impact Co-founders: Fernando Martin-Gullans, Helene Kortschak Country of operation: United States (New York City) Website: www.ellisimpact.org Email address: fernando@ellisimpact.org, helene@ellisimpact.org Seed grant: $84,000 Background (why is this a promising country/angle for an EGI?) While Americans are the global leaders in total charitable giving, with over $450 billion donated annually, they currently give less than 0.5% of it to the most effective charities. Ellis Impact aims to expand effective giving by focusing on high-net-worth individuals (HNWIs) in New York City. Why HNWIs? Charitable donations are overwhelmingly heavy-tailed. For example, less than 5% of donors account for 40% of donations at existing EGIs such as Giving What We Can and Effektiv Spenden. Why New York City? Home to the largest number of millionaires (>350,000) and a GDP of $1.2 trillion, NYC has the highest wealth concentration in the world and is more economically powerful than many countries with existing EGIs (e.g., Netherlands, Sweden, Norway). It also has the highest charitable giving in the US, totaling $20 billion per year in itemized donations. Near-term plans Our first six months will focus on expanding our local network of prospective donors by attending events (e.g., networking events, conferences, galas) and tapping into existing and under-explored communities in NYC (e.g. EA Finance). We plan to further engage prospective donors through 1-1 advising and hosting our first lean, in-person events to raise awareness around effective giving, bring together like-minded individuals, and introduce them to cause area experts. Targets (reach/giving multiplier/etc.) Our minimum goal for our first nine months is to counterfactually raise our seed funding amount in public donations (>$84,000). Our ambitious goal is to raise three times as many counterfactual donations as we received in seed funding (i.e., $252,000). Room for more funding In late July, we raised $84,000 from the Seed Funding Network, allowing us to run for nine months. We would require an additional $20,000 to extend our runway to 12 months, decreasing the risk of having to shut down before we can demonstrate good results since, according to the experience of other HNW donor advisors, donor relationships potentially take longer than nine months to cultivate. Any funding on top of that would be used for hosting and attending additional and higher-quality events to meet and engage prospective donors. If you are interested in supporting our efforts in other ways, we are currently: Looking for warm introductions to prospective donors in NYC as well as super connectors and ambassadors for future warm introductions. If you know someone you think we shou...

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: Funding for programs and events on global catastrophic risk, effective altruism, and other topics, published by GCR Capacity Building team (Open Phil) on August 13, 2024 on The Effective Altruism Forum. Post authors: Eli Rose, Asya Bergal Posting in our capacities as members of Open Philanthropy's Global Catastrophic Risks Capacity Building team. Note: This program, together with our separate program for work that builds capacity to address risks from transformative AI, has replaced our 2021 request for proposals for outreach projects. If you have a project which was in-scope for that program but isn't for either of these, you can apply to our team's general application instead. Apply for funding here. Applications are open until further notice and will be assessed on a rolling basis. This is a wide-ranging call for applications, seeking to fund programs and events in a variety of areas of interest to Open Philanthropy - including effective altruism, global catastrophic risks, biosecurity, AI for epistemics, forecasting, and other areas. In general, if the topic of your program or event falls within one of our GCR focus areas, or if it's similar to work we've funded in the past in our GCR focus areas, it may be a good fit for this program. If you're unsure about whether to submit your application, we'd encourage you to err on the side of doing so. By "programs and events" we mean scholarship or fellowship programs, internships, residencies, visitor programs, courses[1], seminars, conferences, workshops, retreats, etc., including both in-person and online activities. We're open to funding programs or events aimed at individuals at any career stage, and with a wide range of potential purposes, including teaching new skills, providing new career opportunities, offering mentorship, or facilitating networking. Examples of programs and events of this type we've funded before include: Condor Camp, a summer program for Brazilian students interested in existential risk work. The Future of Humanity Institute's Research Scholars Program supporting early-career researchers in global catastrophic risk. Effective Altruism Global, a series of conferences for individuals interested in effective altruism. Future Forum, a conference aimed at bringing together members of several communities interested in emerging technology and the future. A workshop on using AI to improve epistemics, organized by academics from NYU, the Forecasting Research Institute, the AI Objectives Institute and Metaculus. AI-focused work We have a separate call up for work that builds societal capacity to address risks from transformative AI. If your program or event is focused on transformative AI and/or risks from transformative AI, we prefer you apply to that call instead. However, which call you apply to is unlikely to make a difference to the outcome of your application. Application information Apply for funding here. The application form asks for information about you, your project/organization (if relevant), and the activities you're requesting funding for. We're interested in funding both individual/one-off programs and events, and organizations that run or support programs and events. We expect to make most funding decisions within 8 weeks of receiving an application (assuming prompt responses to any follow-up questions we may have). You can indicate on our form if you'd like a more timely decision, though we may or may not be able to accommodate your request. Applications are open until further notice and will be assessed on a rolling basis. 1. ^ To apply for funding for the development of new university courses, please see our separate Course Development Grants RFP. Thanks for listening. To help us out with The Nonlinear Library or to learn more, please visit nonlinear.org