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: SB 1047: Final Takes and Also AB 3211, published by Zvi on August 28, 2024 on LessWrong. This is the endgame. Very soon the session will end, and various bills either will or won't head to Newsom's desk. Some will then get signed and become law. Time is rapidly running out to have your voice impact that decision. Since my last weekly, we got a variety of people coming in to stand for or against the final version of SB 1047. There could still be more, but probably all the major players have spoken at this point. So here, today, I'm going to round up all that rhetoric, all those positions, in one place. After this, I plan to be much more stingy about talking about the whole thing, and only cover important new arguments or major news. I'm not going to get into the weeds arguing about the merits of SB 1047 - I stand by my analysis in the Guide to SB 1047, and the reasons I believe it is a good bill, sir. I do however look at the revised AB 3211. I was planning on letting that one go, but it turns out it has a key backer, and thus seems far more worthy of our attention. The Media I saw two major media positions taken, one pro and one anti. Neither worried itself about the details of the bill contents. The Los Angeles Times Editorial Board endorses SB 1047, since the Federal Government is not going to step up, and using an outside view and big picture analysis. I doubt they thought much about the bill's implementation details. The Economist is opposed, in a quite bad editorial calling belief in the possibility of a catastrophic harm 'quasi-religious' without argument, and uses that to dismiss the bill, instead calling for regulations that address mundane harms. That's actually it. OpenAI Opposes SB 1047 The first half of the story is that OpenAI came out publicly against SB 1047. They took four pages to state its only criticism in what could have and should have been a Tweet: That it is a state bill and they would prefer this be handled at the Federal level. To which, I say, okay, I agree that would have been first best and that is one of the best real criticisms. I strongly believe we should pass the bill anyway because I am a realist about Congress, do not expect them to act in similar fashion any time soon even if Harris wins and certainly if Trump wins, and if they pass a similar bill that supersedes this one I will be happily wrong. Except the letter is four pages long, so they can echo various industry talking points, and echo their echoes. In it, they say: Look at all the things we are doing to promote safety, and the bills before Congress, OpenAI says, as if to imply the situation is being handled. Once again, we see the argument 'this might prevent CBRN risks, but it is a state bill, so doing so would not only not be first bet, it would be bad, actually.' They say the bill would 'threaten competitiveness' but provide no evidence or argument for this. They echo, once again without offering any mechanism, reason or evidence, Rep. Lofgren's unsubstantiated claims that this risks companies leaving California. The same with 'stifle innovation.' In four pages, there is no mention of any specific provision that OpenAI thinks would have negative consequences. There is no suggestion of what the bill should have done differently, other than to leave the matter to the Feds. A duck, running after a person, asking for a mechanism. My challenge to OpenAI would be to ask: If SB 1047 was a Federal law, that left all responsibilities in the bill to the USA AISI and NIST and the Department of Justice, funding a national rather than state Compute fund, and was otherwise identical, would OpenAI then support? Would they say their position is Support if Federal? Or, would they admit that the only concrete objection is not their True Objection? I would also confront them with AB 3211, b...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: The Information: OpenAI shows 'Strawberry' to feds, races to launch it, published by Martín Soto on August 28, 2024 on LessWrong. Two new The Information articles with insider information on OpenAI's next models and moves. They are paywalled, but here are the new bits of information: Strawberry is more expensive and slow at inference time, but can solve complex problems on the first try without hallucinations. It seems to be an application or extension of process supervision Its main purpose is to produce synthetic data for Orion, their next big LLM But now they are also pushing to get a distillation of Strawberry into ChatGPT as soon as this fall They showed it to feds Some excerpts about these: Plus this summer, his team demonstrated the technology [Strawberry] to American national security officials, said a person with direct knowledge of those meetings, which haven't previously been reported. One of the most important applications of Strawberry is to generate high-quality training data for Orion, OpenAI's next flagship large language model that's in development. The codename hasn't previously been reported. Using Strawberry could help Orion reduce the number of hallucinations, or errors, it produces, researchers tell me. That's because AI models learn from their training data, so the more correct examples of complex reasoning they see, the better. But there's also a push within OpenAI to simplify and shrink Strawberry through a process called distillation, so it can be used in a chat-based product before Orion is released. This shouldn't come as a surprise, given the intensifying competition among the top AI developers. We're not sure what a Strawberry-based product might look like, but we can make an educated guess. One obvious idea would be incorporating Strawberry's improved reasoning capabilities into ChatGPT. However, though these answers would likely be more accurate, they also might be slower. Researchers have aimed to launch the new AI, code-named Strawberry (previously called Q*, pronounced Q Star), as part of a chatbot - possibly within ChatGPT - as soon as this fall, said two people who have been involved in the effort. Strawberry can solve math problems it hasn't seen before - something today's chatbots cannot reliably do - and also has been trained to solve problems involving programming. But it's not limited to answering technical questions. When given additional time to "think," the Strawberry model can also answer customers' questions about more subjective topics, such as product marketing strategies. To demonstrate Strawberry's prowess with language-related tasks, OpenAI employees have shown their co-workers how Strawberry can, for example, solve New York Times Connections, a complex word puzzle. But OpenAI's prospects rest in part on the eventual launch of a new flagship LLM it is currently developing, code-named Orion. It isn't clear whether a chatbot version of Strawberry that can boost the performance of GPT-4 and ChatGPT will be good enough to launch this year. The chatbot version is a smaller, simplified version of the original Strawberry model, known as a distillation. However, OpenAI is also using the bigger version of Strawberry to generate data for training Orion, said a person with knowledge of the situation. That kind of AI-generated data is known as "synthetic." It means that Strawberry could help OpenAI overcome limitations on obtaining enough high-quality data to train new models from real-world data such as text or images pulled from the internet. In addition, Strawberry could aid upcoming OpenAI agents, this person said. Using Strawberry to generate higher-quality training data could help OpenAI reduce the number of errors its models generate, otherwise known as hallucinations, said Alex Graveley, CEO of agent startup Minion AI a...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: What Depression Is Like, published by Sable on August 27, 2024 on LessWrong. I was thinking to myself about the difficulties I have explaining depression to people, when I thought of a relatively good metaphor for it. Bear with me. Sudoku For anyone unaware, Sudoku is a puzzle where one tries to fill in a 9x9 grid of numbers according to certain rules: Each row, column, and 3x3 square must have the numbers 1-9 in them, without repeating any numbers. Black numbers are given, red numbers start as blank squares and must be solved by the puzzler. It's a common form of brain teaser, much like a crossword puzzle or logic puzzle. Some Sudoku puzzles are difficult and some are easy; for our purposes we'll think about ones that are relatively easy. Brain App Imagine, for a moment, that someone hacked your brain, and installed an app in it (don't worry about the how). What this app does is force you to - whenever you want to do something - solve a mild Sudoku puzzle first. Not a hard one, it's not difficult, just annoying. Want to get out of bed? Solve a Sudoku puzzle. Want to start work in the morning? Solve a Sudoku puzzle. Want to get dressed, workout, eat, talk to someone, etc.? First you've got to solve the puzzle. At first it's irritating, but you adapt. You figure out shortcuts for solving Sudoku puzzles. It's brainpower you're not expending on anything useful, but you get by. This is the base case, the core of the metaphor. Now we expand it. There are two dimensions along which this nefarious app gets more annoying as time goes on: 1. It decreases the granularity of the actions to which it applies. In other words, where before you had to solve a Sudoku puzzle to go to work, now you've got to solve a puzzle to get dressed, a puzzle to get in the car, a puzzle to drive, and a puzzle to actually get started working. Before all of those counted as a single action - 'go to work' - now they're counted separately, as discrete steps, and each requires a puzzle. 2. It increases the number of puzzles you have to solve to do anything. At first it's just one Sudoku puzzle; eventually, it's two, then three, and so on. Having to solve a single Sudoku puzzle whenever you want to do anything is annoying; having to solve five is downright irritating. So what happens to you - what does your life look like - with this app running in your head? Dimension 1 As the depression gets worse, the granularity of the actions requiring Sudoku solves gets smaller. What does this look like? At first you go through your normal morning routine, except that upon waking up, you need to solve the Sudoku puzzle to get started. Then you have to do a Sudoku puzzle to get out of bed, another to make coffee, another to get dressed, another to shower, and so on. Then you have to do a Sudoku puzzle to open your eyes, another to sit up, another to swing your legs around and another to actually stand up. Finally, each individual muscle contraction comes with its own Sudoku puzzle. Want to sit up? That single action is composed of many pieces: your arms shift to support your weight, your stomach contracts to pull you up, your leg muscles tighten to keep your lower body in place. All of those now require their own puzzles. Each puzzle, on its own, isn't particularly difficult. But they do take some nonzero amount of effort, and when you add that required effort to every single thing you do, suddenly you find yourself doing a lot less. 'Getting out of bed' is now a complicated, multi-step operation that takes way more work than it used to. Solving all these puzzles takes time, too, so you're slower than you used to be at everything. Activities or jobs that you used to breeze through in seconds can stretch into minutes. Parts of your routine that never left you tired now leave you feeling like your brain has been lift...

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: Why Large Bureaucratic Organizations?, published by johnswentworth on August 27, 2024 on LessWrong. Large bureaucratic organizations have long seemed… strange to me, on an intuitive level. Why do they exist? Like, in a world where the median person is John Wentworth ("Wentworld"), I'm pretty sure there just aren't large organizations of the sort our world has. Nobody would ever build such an organization, because they're so obviously wildly inefficient and misaligned. And even if somebody tried, everyone would demand prohibitively high prices to work either for the large organization or with it, since it's just so deeply unpleasant to interface with. Nobody would buy anything sold by such an organization, or vote for such an organization to continue to exist, because the organization as an entity is so obviously both incompetent and untrustworthy. So how on Earth (as opposed to Wentworld) are large organizations stable? The economists have some theorizing on the topic (google "theory of the firm"), but none of it makes me feel much less confused about the sort of large organizations I actually see in our world. The large organizations we see are clearly not even remotely economically efficient; for instance, they're notoriously full of "bullshit jobs" which do not add to the bottom line, and it's not like it's particularly difficult to identify the bullshit jobs either. How is that a stable economic equilibrium?!? In this post I'll present a model which attempts to answer that ball of confusion. The summary is: "Status", in the sense of a one-dimensional dominance hierarchy, is A Thing. We'll call it dominance-status to make it clear that we're not talking about some other kind of status. The way dominance-status normally works in higher animals, newcomers to a group generally enter near the bottom of the hierarchy (even if they were previously high-status in some other group). Within a group, dominance-status is mostly reasonably stable. So, one of the main ways group members can move "up" in dominance-status (i.e. get more members "below" them) without a risky fight, is simply to add more members to the group. Managers at large organizations are mostly motivated by dominance-status. So, the main thing for which managers get de-facto social/cognitive positive reinforcement is increasing their dominance-status and/or avoiding decreases in their dominance-status. Then, the natural prediction is that those managers (at all levels) will tend to add as many people as possible to the hierarchy under them, and minimize firing people, since that's what maximizes their dominance-status. … buuuut the drive to expand the hierarchy is limited by the organization's budget. So in practice, organizations will tend to expand until all the profit is eaten up (in the case of for-profit organizations) or until all the allocated budget is eaten up. And then the hungry managers will fight for more budget. Much of what looks like organizational "inefficiency" and "misalignment" from an standard economic efficiency perspective looks like well-aligned dominance-maximization. … so e.g. large companies or government agencies are basically runaway human monuments of dominance and submission, limited mainly by their budget. There's a lot of steps here, and I'm not super-confident in this model. But when I step into the model, large organizations no longer look strange and confusing; the model seems to generate a remarkably good description of most real large organizations, both private and public. Now let's walk through the model in more detail, starting with relevant background studies. Background: Dominance-Status Empirical Ontology Justification: Dominance-Status Is A Thing "Status" typically connotes a mental model in which we could assign everyone a number/rank, and then some kind of beha...

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: Effective Altruism and the Human Mind: a 5000 Word Summary, published by Alex Savard on August 27, 2024 on The Effective Altruism Forum. by STEFAN SCHUBERT & LUCIUS CAVIOLA Context Earlier this year, @Stefan_Schubert & @Lucius Caviola published Effective Altruism and the Human Mind: The Clash Between Impact and Intuition the first book-length examination of the psychology of effective altruism. This isn't a review but it's a great book and I expect it to become an indispensable (oft-cited) resource for practitioners in the EA ecosystem. Assuming that other busy altruists might not have time to read the full 65,000 word version, I created a 5,000 word summary with the help of Claude Sonnet 3.5 that aims to distill the most important ideas and takeaways. Having compared it to the original text and my own notes I think it's a good summary and could be very useful for some. It's not perfect - but as the book itself reminds us in Chapter 9 - a focus on perfection can be counterproductive! The full book is available for purchase at Oxford University Press, as a free PDF, and as an audiobook thanks to @Aaron Bergman here: Apple Podcasts, Spotify, RSS. Appreciation We owe Schubert & Caviola a great deal for writing such a useful book and owe Caviola even more for doing so much of the supporting research. It all sheds essential light onto an otherwise dark region of our collective understanding. I am humbled by their contributions (and those documented in the book) and merely seek to multiply the impact of that great work by making it slightly more accessible. Enjoy! Contents PART I. OBSTACLES 1. The Norms of Giving 2. Neglecting the Stakes 3. Distant Causes and Nearsighted Feelings 4. Tough Prioritizing 5. Misconceptions About Effectiveness PART II. INTERVENTIONS 1. Information, Nudges, and Incentives 2. Finding the Enthusiasts 3. Fundamental Value Change 4. Effective Altruism for Mortals PART I. OBSTACLES 1. The Norms of Giving The chapter begins by contrasting how people make decisions in different domains of life. For example, when choosing a restaurant, people typically rely on subjective preferences and personal taste. In contrast, when making investment decisions for retirement, people tend to defer to objective data and expert advice. The authors then pose the question: How do people approach decisions when trying to do good in the world? Research by Jonathan Berman and colleagues is presented, showing that most people approach charitable giving more like choosing a restaurant than making an investment decision. They tend to base their choices on personal feelings and preferences rather than objective measures of effectiveness. This tendency persists even when people are explicitly told that one charity is more effective than another. The chapter explores several reasons for this approach to charitable giving: 1. Personal Connections: People often experience a personal connection with specific causes, leading them to support these causes even if they're not the most effective. 2. Urgency: More urgent problems, like disaster relief, tend to evoke stronger emotional responses than ongoing issues, even if the latter might be more cost-effective to address. 3. Failure to Research Effectiveness: Only a small percentage of donors research multiple charities before donating, with many making quick, spontaneous decisions based on gut instincts. The authors argue that these factors contribute to a norm where emotional appeal takes precedence over effectiveness in charitable giving. This norm is reinforced by societal expectations - most people don't criticize others for prioritizing causes they care about over more effective alternatives. The chapter then delves into the philosophical distinction between obligatory and supererogatory actions. Charitable giving is generally vie...

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: Soft Nationalization: How the US Government Will Control AI Labs, published by Deric Cheng on August 27, 2024 on LessWrong. We have yet to see anyone describe a critical element of effective AI safety planning: a realistic model of the upcoming role the US government will play in controlling frontier AI. The rapid development of AI will lead to increasing national security concerns, which will in turn pressure the US to progressively take action to control frontier AI development. This process has already begun,[1] and it will only escalate as frontier capabilities advance. However, we argue that existing descriptions of nationalization[2] along the lines of a new Manhattan Project[3] are unrealistic and reductive. The state of the frontier AI industry - with more than $1 trillion[4] in private funding, tens of thousands of participants, and pervasive economic impacts - is unlike nuclear research or any previously nationalized industry. The traditional interpretation of nationalization, which entails bringing private assets under the ownership of a state government,[5] is not the only option available. Government consolidation of frontier AI development is legally, politically, and practically unlikely. We expect that AI nationalization won't look like a consolidated government-led "Project", but rather like an evolving application of US government control over frontier AI labs. The US government can select from many different policy levers to gain influence over these labs, and will progressively pull these levers as geopolitical circumstances, particularly around national security, seem to demand it. Government control of AI labs will likely escalate as concerns over national security grow. The boundary between "regulation" and "nationalization" will become hazy. In particular, we believe the US government can and will satisfy its national security concerns in nearly all scenarios by combining sets of these policy levers, and would only turn to total nationalization as a last resort. We're calling the process of progressively increasing government control over frontier AI labs via iterative policy levers soft nationalization. It's important to clarify that we are not advocating for a national security approach to AI governance, nor yet supporting any individual policy actions. Instead, we are describing a model of US behavior that we believe is likely to be accurate to improve the effectiveness of AI safety agendas. Part 1: What is Soft Nationalization? Our Model of US Control Over AI Labs We'd like to define a couple terms used in this article: Total nationalization: The traditional meaning of "nationalization", where a government transforms private industry or organizations into a public asset, taking over full ownership and control. Soft nationalization: In contrast to total nationalization, soft nationalization encompasses a wide-ranging set of policy levers governments can use to increase control over the direction, impact, and applications of a private industry or organization. These levers may allow governments to achieve their high-level goals without taking full ownership of said entity. We argue that soft nationalization is a useful model to characterize the upcoming involvement of the US government in frontier AI labs, based on our following observations: 1. Private US AI labs are currently the leading organizations pushing the frontier of AI development, and will be among the first to develop AI with transformative capabilities. 2. Advanced AI will have significant impacts on national security and the balance of global power. 3. A key priority for the US government is to ensure global military and technological superiority - in particular, relative to geopolitical rivals such as China. 4. Hence, the US government will begin to exert greater control and ...

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 Platinum Helps Draw Attention to Japan's Role in Global Health Funding, published by Open Philanthropy on August 27, 2024 on The Effective Altruism Forum. Japan spent more than $19.6 billion on overseas development assistance (ODA) in 2023, making it the third largest single-country donor behind the US and Germany. Open Philanthropy's Global Aid Policy (GAP) team, which is devoted to increasing government aid and guiding it toward more cost-effective approaches, believes there may be opportunities to increase the impact of this aid through targeted advocacy efforts. They estimate that in Western nations like the UK, for every $1,000 spent on ODA, aid advocacy funders spend around $2.60 attempting to support and inform its allocation. Meanwhile, in Japan, advocacy spending is a mere $0.25 for the same amount - more than 10 times less. Accordingly, the GAP program has prioritized work in Japan. The following case study highlights one grantee helping to drive this work forward. ***** One day in March 2023, in the district of Wayanad near India's southern tip, hundreds of villagers lined up for an uncommon service from an unexpected source: a check-up on their lung health, courtesy of Fujifilm. The Japanese company, best known for its cameras, was taking a different kind of picture. Its portable, 3.5 kg battery-powered X-ray machine, designed to deliver hospital-grade diagnostics, enables tuberculosis screenings in regions where medical facilities usually lack the necessary technology. This scene was just one stop on an illuminating trip to India for a group of Japanese journalists and youth activists. From Toyota Tsusho's Sakra World Hospital to Eisai's efforts to combat neglected tropical diseases (NTDs) in Yarada village, each site visit highlighted Japanese businesses and researchers contributing to global health initiatives. Recognizing this opportunity, Open Philanthropy supported Platinum, a Tokyo-based PR firm, in organizing a trip across India aimed at boosting the Japanese public's awareness of urgent global health issues, particularly tuberculosis and neglected tropical diseases (NTDs). Sixteen people attended: six journalists, representing outlets ranging from a long-running daily newspaper to a popular economics broadcast, and 10 youth activists sourced from PoliPoli's Reach Out Project, an Open Philanthropy-funded initiative that incubates charities focused on global health advocacy. Our Senior Program Officer for Global Aid Policy, Norma Altshuler, thought the initiative was timely given recent trends in Japan's ODA spending. Between 2019 and 2022, the share of Japanese ODA allocated to global health doubled (or tripled, including COVID-19 relief). To sustain this momentum, Open Philanthropy is supporting Japanese groups that aim to preserve or grow Japan's commitment to prioritizing global health initiatives. In a post-trip interview with Open Philanthropy, Soichi Murayama, who helped organize the trip, says one challenge of Japan's media landscape "is that Japanese media doesn't cover global health very often." Murayama attributes the dearth of dedicated coverage to limited reader interest, creating a feedback loop where minimal reporting leads to low awareness, which in turn reduces appetite for such stories. Ryota Todoroki, a medical student who participated in the trip, echoes this sentiment: "NTDs are often seen as a foreign issue with no relevance to Japan, so changing this perception is a major challenge." The Fujifilm initiative in Wayanad provides an example of how connecting Japanese companies to global health efforts can help illustrate the impact of foreign aid. This approach not only highlights Japan's technological contributions but also links economic interests with humanitarian efforts. To gauge the impact of awareness campaigns, PR 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: What is it to solve the alignment problem?, published by Joe Carlsmith on August 27, 2024 on LessWrong. People often talk about "solving the alignment problem." But what is it to do such a thing? I wanted to clarify my thinking about this topic, so I wrote up some notes. In brief, I'll say that you've solved the alignment problem if you've: 1. avoided a bad form of AI takeover, 2. built the dangerous kind of superintelligent AI agents, 3. gained access to the main benefits of superintelligence, and 4. become able to elicit some significant portion of those benefits from some of the superintelligent AI agents at stake in (2).[1] The post also discusses what it would take to do this. In particular: I discuss various options for avoiding bad takeover, notably: Avoiding what I call "vulnerability to alignment" conditions; Ensuring that AIs don't try to take over; Preventing such attempts from succeeding; Trying to ensure that AI takeover is somehow OK. (The alignment discourse has been surprisingly interested in this one; but I think it should be viewed as an extreme last resort.) I discuss different things people can mean by the term "corrigibility"; I suggest that the best definition is something like "does not resist shut-down/values-modification"; and I suggest that we can basically just think about incentives for/against corrigibility in the same way we think about incentives for/against other types of problematic power-seeking, like actively seeking to gain resources. I also don't think you need corrigibility to avoid takeover; and I think avoiding takeover should be our focus. I discuss the additional role of eliciting desired forms of task-performance, even once you've succeeded at avoiding takeover, and I modify the incentives framework I offered in a previous post to reflect the need for the AI to view desired task-performance as the best non-takeover option. I examine the role of different types of "verification" in avoiding takeover and eliciting desired task-performance. In particular: I distinguish between what I call "output-focused" verification and "process-focused" verification, where the former, roughly, focuses on the output whose desirability you want to verify, whereas the latter focuses on the process that produced that output. I suggest that we can view large portions of the alignment problem as the challenge of handling shifts in the amount we can rely on output-focused verification (or at least, our current mechanisms for output-focused verification). I discuss the notion of "epistemic bootstrapping" - i.e., building up from what we can verify, whether by process-focused or output-focused means, in order to extend our epistemic reach much further - as an approach to this challenge.[2] I discuss the relationship between output-focused verification and the "no sandbagging on checkable tasks" hypothesis about capability elicitation. I discuss some example options for process-focused verification. Finally, I express skepticism that solving the alignment problem requires imbuing a superintelligent AI with intrinsic concern for our "extrapolated volition" or our "values-on-reflection." In particular, I think just getting an "honest question-answerer" (plus the ability to gate AI behavior on the answers to various questions) is probably enough, since we can ask it the sorts of questions we wanted extrapolated volition to answer. (And it's not clear that avoiding flagrantly-bad behavior, at least, required answering those questions anyway.) Thanks to Carl Shulman, Lukas Finnveden, and Ryan Greenblatt for discussion. 1. Avoiding vs. handling vs. solving the problem What is it to solve the alignment problem? I think the standard at stake can be quite hazy. And when initially reading Bostrom and Yudkowsky, I think the image that built up most prominently i...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: My Apartment Art Commission Process, published by jenn on August 27, 2024 on LessWrong. When I know that I'm going to be moving out from an apartment soon, I commission a digital artist to draw it for me. Then I print it out and I have a cool art piece. If you love your current place but you don't think you'll spend the rest of your life there, you should consider doing the same. Digital artists are much cheaper than I think they should be. I've paid artists between $200-$500 CAD[1] for my commissions, generally spread across one or two additional housemates. (You should expect to pay more - I limit my own commissions to the common areas since my bedrooms tend to be very plain, and solely used for sleep and other private activities. Also inflation exists.) You can also consider hiring artists from developing countries if you want your dollar to go further, but I don't have any advice on how to seek those folks out specifically. You'll be looking at around 10 hours of effort on your end, frontloaded but spread out across 2-4 months. I detail my process below. But first, here are the pieces that I've commissioned so far: Aren't they sick as hell??? I love them so much. Okay, let's get you started on yours. I'll stick a sample email script at the bottom too. Commissioning An Art Of Your Living Space, Step By Step 1. come up with a budget talk to your roomies if you have them, and come up with a price you're willing to pay altogether. i think my apartment commissions are probably 15-30? hours of work, multiply that by how much you're willing to pay a skilled artisan for an hour of work. (i should note that in 3/3 cases for me, the minimum budget ended up being like 30-100% more than what the artist was willing to accept. digital artists often decline to charge reasonable rates for their labour.) 2. find 2-3 viable artists endorsed strategies involve browsing r/wimmelbilder, the twitter/tumblr hashtag #isometricart, and google imagesing "isometric apartment layout" and clicking around. for maximal exposure to artists that are open to work, search dribbble.com for "isometric", but note that the pickings there are fairly slim. in many isometric tags i find a lot of rendered stuff but i prefer to go for more trad art forms as i expect renderings to be more time consuming (expensive), harder to edit, and worse for the amount of detail i want[2]. also, you don't need to commission specifically an isometric piece! you can go wild at this step finding any artist who illustrates interiors in a way you like. while browsing, it could be a good idea to save sample images that you like; you can then pass them on to the artist of your choice as reference for what kind of art appeals to you. find artists whose work make you feel actively excited, when you think about having your own apartment done in their style. check out the portfolios of artists you like. you're looking for portfolios with a pretty solid number of pieces, ideally at least like ~5 years of stuff, and maybe a consistent style if it's a style you like. new artists could be high variance, and for all you know you might be messaging a talented 15 year old who will drop you like a hot potato when they need to start studying for an exam in earnest (my little brother has turned down commission inquiries for this reason when he was in high school). i don't think AI art is good enough to do this kind of work yet, so I'd stick with traditional digital (lol) artists for now. 3. email the viable artists email the artists whose portfolios passed the vibe check, letting them know what you want to commission them for and your budget, and asking for a quote if they are open to working with you. having 2-3 artists on hand here is good because it's kind of 50/50 if any particular artist online is accepting commissions. don't take it p...

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: The most basic rationality techniques are often neglected, published by Vasco Grilo on August 27, 2024 on The Effective Altruism Forum. This is a crosspost for The most basic rationality techniques are often neglected and Rationality and discipline by Stefan Schubert, published on 30 August and 15 September 2021. The most basic rationality techniques are often neglected and Rationality and discipline How much can we "debias" ourselves? What should we do to become more rational? When discussing those issues, people usually focus on sophisticated debiasing techniques ("pre-mortem") and advanced concepts from epistemology and statistics. What's often forgotten is that we already have a bunch of very simple but effective techniques for improving our rationality, such as (cf. Grice's maxims): "Don't believe things for which you lack evidence. And don't say such things in discussions." "Don't make irrelevant personal attacks." "Don't attack straw men." "Define your terms clearly." "Make one point at a time." "Try not to be too emotional when thinking about heated issues." It seems to me that irrationality regarding moral and political issues (arguably the most important form of irrationality) is very often due to failure to apply these very simple techniques. That was certainly my experience when I argument-checked opinion pieces and election debates. Most fallacies I identified were extremely basic and boring (cf. my post Frequent fallacies). Maybe the most common was failure to provide evidence for claims that need evidence. So maybe what we need to do to make people more rational isn't primarily to teach them sophisticated debiasing techniques and advanced concepts. They are costly to learn, and most people have other, more pressing things to attend to. People who suggest new interventions and social reforms often neglect such time and attention costs. One might also suspect that people focus on the more sophisticated rationality techniques partly because they find them more interesting to think about than the basic and boring ones. Instead, maybe we should focus on getting people to apply the most basic techniques consistently. Some of the sophisticated techniques are no doubt useful, but I'm not sure the primary focus should be on them. To make people actually use these basic techniques, what's needed is strong social norms, saying that you shouldn't believe or say things you lack evidence for, that you should define your terms clearly, etc. The strength of such norms have varied a lot over the course of history - and still varies today across different contexts. And my sense is that people's actual rationality by and large reflects the strength of those rationality norms. So these norms can be pushed more or less, and I would guess that they are not yet pushed as much as they realistically could be pushed. Still, it's obviously a difficult task, and I'm unsure about how to best go about it. (This post was first posted on Facebook, 3 February 2020. Slightly revised.) Rationality and discipline Rationality has many aspects. It seems to me that the rationalist community often focuses on the fun bits, such as self-improvement, musings on one's own thought-processes, and speculative theorising (though no doubt there are important exceptions). What then gets a bit lost is that rationality is to a large extent about discipline, restraint, and rigour: things that aren't necessarily fun for most people. This is maybe natural given that the community is at least partly built around an intrinsic interest in rationality - they normally don't provide strong extrinsic incentives (e.g. degrees, money) to students of rationality. Nevertheless, I think a stronger emphasis on these less intrinsically appealing aspects of rationality is important. From Facebook, 16 January 2020. Thanks...