The Nonlinear Library: LessWrong

Link to original articleWelcome 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 AI Race With China Can Be Better Than Not Racing, published by niplav on July 2, 2024 on LessWrong. Frustrated by all your bad takes, I write a Monte-Carlo analysis of whether a transformative-AI-race between the PRC and the USA would be good. To my surprise, I find that it is better than not racing. Advocating for an international project to build TAI instead of racing turns out to be good if the probability of such advocacy succeeding is 20%. A common scheme for a conversation about pausing the development of transformative AI goes like this: Abdullah: "I think we should pause the development of TAI, because if we don't it seems plausible that humanity will be disempowered by by advanced AI systems." Benjamin: "Ah, if by "we" you refer to the United States (and and its allies, which probably don't stand a chance on their own to develop TAI), then the current geopolitical rival of the US, namely the PRC, will achieve TAI first. That would be bad." Abdullah: "I don't see how the US getting TAI first changes anything about the fact that we don't know how to align superintelligent AI systems - I'd rather not race to be the first person to kill everyone." Benjamin: "Ah, so now you're retreating back into your cozy little motte: Earlier you said that "it seems plausible that humanity will be disempowered", now you're acting like doom and gloom is certain. You don't seem to be able to make up your mind about how risky you think the whole enterprise is, and I have very concrete geopolitical enemies at my (semiconductor manufacturer's) doorstep that I have to worry about. Come back with better arguments." This dynamic is a bit frustrating. Here's how I'd like Abdullah to respond: Abdullah: "You're right, you're right. I was insufficiently precise in my statements, and I apologize for that. Instead, let us manifest the dream of the great philosopher: Calculemus! At a basic level, we want to estimate how much worse (or, perhaps, better) it would be for the United States to completely cede the race for TAI to the PRC. I will exclude other countries as contenders in the scramble for TAI, since I want to keep this analysis simple, but that doesn't mean that I don't think they matter. (Although, honestly, the list of serious contenders is pretty short.) For this, we have to estimate multiple quantities: 1. In worlds in which the US and PRC race for TAI: 1. The time until the US/PRC builds TAI. 2. The probability of extinction due to TAI, if the US is in the lead. 3. The probability of extinction due to TAI, if the PRC is in the lead. 4. The value of the worlds in which the US builds aligned TAI first. 5. The value of the worlds in which the PRC builds aligned TAI first. 2. In worlds where the US tries to convince other countries (including the PRC) to not build TAI, potentially including force, and still tries to prevent TAI-induced disempowerment by doing alignment-research and sharing alignment-favoring research results: 1. The time until the PRC builds TAI. 2. The probability of extinction caused by TAI. 3. The value of worlds in which the PRC builds aligned TAI. 3. The value of worlds where extinction occurs (which I'll fix at 0). 4. As a reference point the value of hypothetical worlds in which there is a multinational exclusive AGI consortium that builds TAI first, without any time pressure, for which I'll fix the mean value at 1. To properly quantify uncertainty, I'll use the Monte-Carlo estimation library squigglepy (no relation to any office supplies or internals of neural networks). We start, as usual, with housekeeping: As already said, we fix the value of extinction at 0, and the value of a multinational AGI consortium-led TAI at 1 (I'll just call the consortium "MAGIC", from here on). That is not to say that the MAGIC-led TAI future is the best possible TAI future...

Link to original articleWelcome 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: Decomposing the QK circuit with Bilinear Sparse Dictionary Learning, published by keith wynroe on July 2, 2024 on LessWrong. This work was produced as part of Lee Sharkey's stream in the ML Alignment & Theory Scholars Program - Winter 2023-24 Cohort Intro and Motivation Sparse dictionary learning (SDL) has attracted a lot of attention recently as a method for interpreting transformer activations. They demonstrate that model activations can often be explained using a sparsely-activating, overcomplete set of human-interpretable directions. However, despite its success for explaining many components, applying SDL to interpretability is relatively nascent and have yet to be applied to some model activations. In particular, intermediate activations of attention blocks have yet to be studied, and provide challenges for standard SDL methods. The first challenge is bilinearity: SDL is usually applied to individual vector spaces at individual layers, so we can simply identify features as a direction in activation space. But the QK circuits of transformer attention layers are different: They involve a bilinear form followed by a softmax. Although simply applying sparse encoders to the keys and queries[1] could certainly help us understand the "concepts" being used by a given attention layer, this approach would fail to explain how the query-features and key-features interact bilinearly. We need to understand which keys matter to which queries. The second challenge is attention-irrelevant variance: A lot of the variance in the attention scores is irrelevant to the attention pattern because it is variance in low scores which are softmaxed to zero; this means that most of the variability in the keys and queries is irrelevant for explaining downstream behaviour[2]. The standard method of reconstructing keys and queries would therefore waste capacity on what is effectively functionally irrelevant noise. To tackle these two problems (bilinearity and attention-irrelevant variance), we propose a training setup which only reconstructs the dimensions of the keys and queries that most affect the attention pattern. Training Setup Our training process has two steps: Step 1: Reconstructing the attention pattern with key- and query- encoder-decoder networks Step 2: Finding a condensed set of query-key feature pairs by masking Step 1: Reconstructing the attention pattern with key- and query-transcoders Architecture Our first training step involves training two sparse dictionaries in parallel (one for the keys and one for the queries). The dictionaries both take in the layer-normalized residual stream at a given layer (normalised_resid_pre_i) and each output a [n_head * d_head] vector, representing the flattened keys and queries[3]. Figure 1: High-level diagram of our training set-up Loss functions However, rather than penalising the reconstruction loss of the keys and queries explicitly, we can use these keys and queries to reconstruct the original model's attention pattern. To train the reconstructed attention pattern, we used several different losses: KL divergence between the attention pattern (using reconstructed keys and reconstructed queries) and the ground-truth attention pattern produced by the original model. We also added two auxiliary reconstruction losses both for early-training-run stability, and to ensure our transcoders do not learn to reconstruct the keys and queries with an arbitrary rotation applied (since this would still produce the same attention scores and patterns): KL divergence between the attention pattern (using reconstructed keys and the original model's queries) and the ground-truth attention pattern produced by the original model. KL divergence between the attention pattern (using the original models' keys and the reconstructed queries) and the ground-truth atten...

Link to original articleWelcome 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: In Defense of Lawyers Playing Their Part, published by Isaac King on July 2, 2024 on LessWrong. This is a linkpost for In Defense of Lawyers Playing Their Part. Michael Huemer writes about why he believes it's wrong for lawyers to pursue unjust legal outcomes. It's a good article, and one of the best defenses of this position I've seen. Still, I think this argument is mistaken. The reason why we require lawyers to fight for "their side" even if they believe they're in the wrong is to minimize the opportunity for bias. Imagine if all trials were bench trials, decided by only one person as the judge. Even if they're taught to be as objective as possible, there would still be significant concerns about unconscious bias. One person only has one set of experiences to draw on, which is necessarily not very representative of the full range of experiences. And in some ways this problem becomes worse the more training the judge is given, since it filters the pool of valid people down to a small subset of the population. The chosen solution to this is to instead have the important cases decided by a jury, randomly[1] selected from the population. The jury is then instructed that they must come to a unanimous decision, and are allowed an arbitrarily-long time to discuss the case. This prevents a tyranny of the majority, while still allowing a diverse range of perspectives to have a voice in the discussion. Any prospective juror who seems likely to be so biased that they would vote in a predetermined way regardless of the evidence is removed from consideration during voir dire. (This step does reduce the representativeness of the jury, but the assumption is that for any group of people who hold a particular perspective, there will be members of that group who are not so biased as to be selected out.[2]) But this doesn't solve all problems. The jury is still only human, and if they're presented with facts that are biased in only one direction, they're more likely to vote in that direction. If lawyers were instructed to present an unbiased case to the jury, this would provide a significant incentive for the less ethical lawyers to not do as instructed, using a misleading presentation of data to bias the jury towards their side. This is a bad incentive to give people. It would also lead to copious accusations from the losing side that the other side's lawyer was presenting biased facts, which would necessitate some process to sort them out every time, even if both lawyers were perfectly objective. So instead, we tell the lawyers to go nuts. Be as biased as possible, and, as long as they're equally skilled and there aren't background factors that favor one position over the other, this ensures that each presented position is equally far from the truth. The jury now has a fair overview of both sides of the case, without a malicious lawyer being able to advantage one over the other.[3] Michael provides 5 arguments in favor of this position - that lawyers are obligated to do their best even for a client they believe is guilty - then attempts to refute them all. I'll go through them individually. 2.1. The epistemological problem Michael argues that lawyers can know with high confidence that their clients are guilty, giving the example of Benjamin Courvoisier. Thus, "I'm not sure so I should just defend my client" is not an excuse. In the case of Benjamin Courvoisier, Benjamin confessed to the lawyer, presumably under the expectation that the lawyer would not publicly share this information. If lawyers were duty-bound to share any private confession given to them, all but the dumbest criminals would simply stop giving private confessions. The overall effect on convictions would be negligible. But cases like Benjamin Courvoisier are few and far between. Using this example to argue that de...

Link to original articleWelcome 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: Important open problems in voting, published by Closed Limelike Curves on July 2, 2024 on LessWrong. Strategy-resistance Identify, or prove impossibility, of a voting system which incentivizes 1. A strictly sincere ranking of all candidates in the zero-information setting, where it implements a "good" social choice rule such as the relative (normalized) utilitarian rule, a Condorcet social choice rule, or the Borda rule. 2. In a Poisson game or similar setting: a unique semi-sincere Nash equilibrium that elects the Condorcet winner (if one exists), similar to those shown for approval voting by Myerson and Weber (1993) and Durand et al. (2019). Properties of Multiwinner voting systems There's strikingly little research on multiwinner voting systems. You can find a table of criteria for single-winner systems on Wikipedia, but if you try and find the same for multi-winner systems, there's nothing. Here's 9 important criteria we can judge multiwinner voting systems on: 1. Independence of Irrelevant Alternatives 2. Independence of Universally-Approved Candidates 3. Monotonicity 4. Participation 5. Precinct-summability 6. Polynomial-time approximation scheme 7. Proportionality for solid coalitions 8. Perfect representation in the limit 9. Core-stability (may need to be approximated within a constant factor) I'm curious which combinations of these properties exist. Probabilistic/weighted voting systems are allowed. Thanks for listening. To help us out with The Nonlinear Library or to learn more, please visit nonlinear.org

Link to original articleWelcome 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 Executive Team & Board - PIBBSS, published by Nora Ammann on July 1, 2024 on LessWrong. TLDR: PIBBSS is changing its core team. Nora is stepping down as director due to joining ARIA, and Lucas Teixeira and Dusan Nesic are taking over her leadership role. Nora joins the board, alongside Tan Zhi Xuan, Alexander Gietelink Oldenziel, Ben Goldhaber and Gabriel Weil. I (Nora) have recently accepted an offer to join ARIA's Safeguarded AI Programme as Technical Specialist under davidad. As such, I am stepping back as Director at PIBBSS, after co-founding and leading PIBBSS since 2021. It wasn't an easy choice to make! I deeply care about and believe in the mission of and the people at PIBBSS. Before davidad encouraged me to apply for the role, I hadn't considered leaving PIBBSS. I believe PIBBSS is playing an important role in terms of fostering theoretically ambitious and empirically grounded AI safety research. I am very excited about the directions the team and I have been forging, and extremely impressed by the quality of talent we've recently been able to attract. I strongly believe that PIBBSS is in the position to make important and neglected contributions in both research and field-building for AI safety. The team and I have been reflecting on and preparing for this transition for a while. Thanks to that, I am confident that Lucas & Dušan will do a great job at shepherding PIBBSS through this transition, and beyond! We have done our homework, and I feel grateful about being able to put so much my trust into this team. As such, Lucas & Dušan will collectively form the new Executive Team. Dušan has been leading PIBBSS' operations for the last ~2 years and has developed a deep familiarity with everything involved in making the organization run smoothly. Lucas, who joined us a bit over 8 months ago, has been acting as research manager and collaborator for our research affiliates. Going forward, Dušan continues to be in charge of all operational matters, and Lucas will be leading the research activities. Together, we have made significant progress in clarifying and moving towards our updated research & field building vision over the last number of months. In order to further support this transition, and strengthen PIBBSS in pursuing its ambitious plans, we have also set up a board. We're pleased to have the following people join the board (in addition to myself): Tan Zhi Xuan Alexander Gietelink Oldenziel Ben Goldhaber Gabriel Weil I am immensely grateful to my team, our affiliates, our current and past fellows, and all the many wonderful collaborators and 'friends of PIBBSS' over the years! And I am excited to be able to continue supporting PIBBSS from my new position on the board. Thanks for listening. To help us out with The Nonlinear Library or to learn more, please visit nonlinear.org

Link to original articleWelcome 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: Datasets that change the odds you exist, published by dynomight on June 30, 2024 on LessWrong. 1. It's October 1962. The Cuban missile crisis just happened, thankfully without apocalyptic nuclear war. But still: Apocalyptic nuclear war easily could have happened. Crises as serious as the Cuban missile crisis clearly aren't that rare, since one just happened. You estimate (like President Kennedy) that there was a 25% chance the Cuban missile crisis could have escalated to nuclear war. And you estimate that there's a 4% chance of an equally severe crisis happening each year (around 4 per century). Put together, these numbers suggest there's a 1% chance that each year might bring nuclear war. Small but terrifying. But then 62 years tick by without nuclear war. If a button has a 1% chance of activating and you press it 62 times, the odds are almost 50/50 that it would activate. So should you revise your estimate to something lower than 1%? 2. There are two schools of thought. The first school reasons as follows: Call the yearly chance of nuclear war W. This W is a "hidden variable". You can't observe it but you can make a guess. But the higher W is, the less likely that you'd survive 62 years without nuclear war. So after 62 years, higher values of W are less plausible than they were before, and lower values more plausible. So you should lower your best estimate of W. Meanwhile, the second school reasons like this: Wait, wait, wait - hold on. If there had been nuclear war, you wouldn't be here to calculate these probabilities. It can't be right to use data when the data can only ever pull you in one direction. So you should ignore the data. Or at least give it much less weight. Who's right? 3. Here's another scenario: Say there's a universe. In this universe, there are lots of planets. On each planet there's some probability that life will evolve and become conscious and notice that it exists. You're not sure what that probability is, but your best guess is that it's really small. But hey, wait a second, you're a life-form on a planet with conscious life! Given that you exist, should you increase your guess for how likely conscious life is to evolve on a random planet? Again, you have two schools of thought. One says yes, you have data, increase your guess, while the other says no, don't increase, if there wasn't life you wouldn't be here, anthropic principle - anthropic principle! 4. After many years of being confused by these questions, I think I now understand what's happening. These questions are confusing because they're actually about a sort of narrow technical question, and only appear to be about to the fact that you might not exist. To explain, let me introduce another scenario: One day you wake up at my house. As you groggily look around, I explain that you've been invited to Dynomight family dinner! And that the way that DFD works is: 1. I sneak into your house at night, anesthetize you, and bring you to my lair. 2. When you wake up, I make you some delicious Fagioli all'Uccelletto. 3. After you've eaten, I bring out a box containing a bunch of identical revolvers. Half have no bullets in them, while the other half have bullets in all six chambers. You pick one revolver at random, put it to your head, and pull the trigger. (To refuse would be a huge faux pas.) 4. If you're still alive, I bring out a $100 bill and offer to sell it to you for $60. If you agree, I take your gun and see if it has bullets in it. If it's empty, then I take your $60, give you the $100, and ask you to come back soon. If not, I take your $60 but don't give you the $100, welcome to dinner at my house, chump. So you eat the Fagioli all'Uccelletto (it is excellent) and you play the mandatory revolver game and don't die, and I offer you the $100. Should you accept? Yes you should. There's ...

Link to original articleWelcome 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 Incredible Fentanyl-Detecting Machine, published by sarahconstantin on June 29, 2024 on LessWrong. There's bound to be a lot of discussion of the Biden-Trump presidential debates last night, but I want to skip all the political prognostication and talk about the real issue: fentanyl-detecting machines. Joe Biden says: And I wanted to make sure we use the machinery that can detect fentanyl, these big machines that roll over everything that comes across the border, and it costs a lot of money. That was part of this deal we put together, this bipartisan deal. More fentanyl machines, were able to detect drugs, more numbers of agents, more numbers of all the people at the border. And when we had that deal done, he went - he called his Republican colleagues said don't do it. It's going to hurt me politically. He never argued. It's not a good bill. It's a really good bill. We need those machines. We need those machines. And we're coming down very hard in every country in Asia in terms of precursors for fentanyl. And Mexico is working with us to make sure they don't have the technology to be able to put it together. That's what we have to do. We need those machines. Wait, what machines? You can remotely, non-destructively detect that a bag of powder contains fentanyl rather than some other, legal substance? And you can sense it through the body of a car? My god. The LEO community must be holding out on us. If that tech existed, we'd have tricorders by now. What's actually going on here? What's Up With Fentanyl-Detecting Machines? First of all, Biden didn't make them up. This year, the Department of Homeland Security reports that Customs and Border Patrol (CBP) has deployed "Non-Intrusive Inspection" at the US's southwest border: "By installing 123 new large-scale scanners at multiple POEs along the southwest border, CBP will increase its inspection capacity of passenger vehicles from two percent to 40 percent, and of cargo vehicles from 17 percent to 70 percent." In fact, there's something of a scandal about how many of these scanners have been sitting in warehouses but not actually deployed. CBP Commissioner Troy Miller complained to NBC News that the scanners are sitting idle because Congress hasn't allocated the budget for installing them. These are, indeed, big drive-through machines. They X-ray cars, allowing most traffic to keep flowing without interruption. Could an X-ray machine really detect fentanyl inside a car? To answer that, we have to think about what an x-ray machine actually does. An X-ray is a form of high-energy, short-wavelength electromagnetic radiation. X-rays can pass through solid objects, but how easily they pass through depends on the material - higher atomic number materials are more absorbing per unit mass. This is why bones will show up on an X-ray scan. The calcium (element 20) in bones has higher atomic mass than the other most common elements in living things (carbon, hydrogen, oxygen, nitrogen, sulfur), and bones are also denser than soft tissue, so bones absorb X-rays while the rest of the body scatters it. This is also how airport security scans baggage: a cabinet x-ray shows items inside a suitcase, differentiated by density. It's also how industrial CT scans can look inside products nondestructively to see how they're made. To some extent, X-ray scanners can distinguish materials, by their density and atomic number. But fentanyl is an organic compound - made of carbon, hydrogen, nitrogen, and oxygen, just like lots of other things. Its density is a very normal 1.1 g/mL (close to the density of water.) I'm pretty sure it's not going to be possible to tell fentanyl apart from other things by its density and atomic number alone. Indeed, that's not what the scanner vendors are promising to do. Kevin McAleenam, the former DHS secretary who...

Link to original articleWelcome 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 a chip is designed, published by YM on June 28, 2024 on LessWrong. Disclaimer: This is highly incomplete. I am not an expert in the field. There might be some unfamiliar terms. While I will try to explain things, explaining every single term would be beyond this post. You will usually be able to get a sufficient understanding by clicking the links or googling it. Introduction I think everyone, if they read about the chip industry long enough, has a moment where they have to put down a book or pause a podcast and simply remain stunned at the fact that it is possible to design and build something that is so incredibly impressive. The Apple A17 chip contains 183 million transistors per square millimeter. All placed in a coherent manner and produced with extremely high reliability. This is exactly why it is so fascinating to learn more about how it is actually done. On top of that, in a universe where compute is arguably the most important input in the AI production function, this knowledge is also crucial to effective AI governance. So what follows is a quick introduction to the processes of getting a chip from a vague idea to sending your files to the manufacturer, also called the tape-out. Background Knowledge One of the most important decisions, a decision that significantly determines all the others, is what manufacturer will build your chip and what process they will use. There are companies that do both design and manufacturing (e.g. Intel), but especially when it comes to the most advanced logic chips, more and more companies are what is called " fabless" - they focus on the design and task a so-called "foundry" (e.g. TSMC) with the manufacturing. Nowadays many fabs and fabless companies work together very closely in what is called Design-Technology Co-Optimization (DTCO). In practice, there are quite significant limitations in chip design, and the fab will check design plans and inform designers what can and can't be manufactured. This collaborative approach ensures that chip designs are optimized for the specific manufacturing process, balancing performance, power, area, and yield considerations. DTCO has become increasingly important as the industry approaches the physical limits of semiconductor scaling, requiring closer integration between design teams and process engineers to continue advancing chip capabilities. The foundry sends the design company what is called the process design kit ( PDK ), which contains all the important specifics to the fab and the manufacturing process (also known as the technology node). One factor that in large part determines the profitability of a chip is the yield of the manufacturing process. The yield is the fraction of chips produced that work flawlessly and can be sold. Compared to other types of products, in the semiconductor industry the yield is quite low, sometimes moving significantly below 50% for periods of time, especially at the beginning of a new technology node. To improve yield, optimal manufacturability is taken into account at many stages of the design process in what is called Design for Manufacturability (DFM). Chips are also designed to be easy to test (Design For Testability, DFT). In this post we are focussing on the design process, not with the actual manufacturing steps or the details of a transistor. But it is important to know that in practice we are working with standard cells that are all equal in height and vary in width. varies to make design and manufacturing easier. Often the IP for the standard cells is licensed from third parties. The Design Process My stages follow the outline given by Prof. Adam Teman in this lecture. Definition and Planning This is the stage where we think about what you even want to build. What bus structure do you want? How many cores should it have? What amount of p...

Link to original articleWelcome 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 birds sense magnetic fields, published by bhauth on June 28, 2024 on LessWrong. introduction It is known that many birds are able to sense the direction of Earth's magnetic field. Here's a wikipedia page on that general phenomenon. There have been 2 main theories of how that works. One theory is that birds have magnets in their beak that act like a compass. We know this is the correct theory because: Small magnetite crystals have been found in bird beaks. Anaesthesia of bird beaks seems to affect their magnetic sense, sometimes. The other theory is that birds have some sensing mechanism in their eyes that uses magneto-optical effects. We know this is the correct theory because: Birds can't sense magnetic field direction in red light. Covering the right eye of birds prevents them from sensing field direction. We also know those theories probably aren't both correct because: Most animals don't have a magnetic field sense. It's implausible that birds developed two separate and redundant systems for sensing magnetic fields when other animals didn't develop one. organic magneto-optics It's possible for magnetic fields to affect the optical properties of molecules; here's an example, a fluorescent protein strongly affected by a small magnet. However, known examples of this require much stronger (~1000x) fields than the Earth's magnetic field. Let's suppose birds sense magnetic fields using some proteins in their eyes that directly interact with fields. The energy density of a magnetic field is proportional to the field strength^2. The energy of interaction of a magnet with a field is proportional to the product of the field strengths. The earth has a field of 25 to 65 μT. If we consider the energy of a strongly magnetic protein interacting with the Earth's magnetic field, that's not enough energy to directly cause a cellular signalling effect. So, magnetic fields must act to control some energy-transferring process, and the only logical possibilities are light absorption/emission and transfer of excited states between molecules. Birds can sense the direction of magnetic fields, more so than field strength, so the effect of magnetic fields must be relative to the orientation of something. Molecules are randomly oriented, but absorption/emission of a photon is relative to molecule orientation, so magnetic fields can create differences in absorption/emission of light at different angles. (That's the basis of a spectroscopy technique I previously proposed.) For excited states of molecules to interact with a magnetic field, they must have a magnetic field. The excited states with the strongest fields would logically be triplet states, where the spin of an electron is reversed, creating a net spin difference of 2. (The magnetism of iron comes from the spin of its one unpaired electron, so triplet states are more magnetic than iron atoms.) Molecules absorb/emit photons only of specific wavelengths: as energy and momentum are conserved, molecules must have a vibrational mode that matches the photon. Magnetic fields can shift what wavelengths are absorbed. Considering the energy density of the Earth's magnetic field and the magnetic field of triplet states, shifting the affected wavelengths of visible light by 1nm seems feasible. A blue sky doesn't seem to have sharp enough spectral lines. Can one be made artificially? It's not normally possible to absorb a wide spectrum of light and emit a narrow spectral line: thermodynamically, a more narrow spectrum has a higher "temperature". The spectral width of emission is typically about the same as the width of absorption. (This is why early laser types are so inefficient: they only absorb a small fraction of the light used to pump them. Systems using diode lasers are more efficient.) Thus, we need to absorb only a narrow spectral lin...

Link to original articleWelcome 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: Childhood and Education Roundup #6: College Edition, published by Zvi on June 28, 2024 on LessWrong. Childhood roundup #5 excluded all developments around college. So this time around is all about issues related to college or graduate school, including admissions. Tuition and Costs What went wrong with federal student loans? Exactly what you would expect when you don't check who is a good credit risk. From a performance perspective, the federal government offered loans to often-unqualified students to attend poor-performing, low-value institutions. Those students then did not earn much and were often unable to repay the loans. The students are victims here too, as we told them to do it. Alas, none of the proposed student loan solutions involve fixing the underlying issue. If you said 'we are sorry we pushed these loans on students and rewarded programs and institutions that do not deserve it, and we are going to stop giving loans for those programs and institutions and offer help to the suffering former students, ideally passing some of those costs on to the institutions' then I would understand that. Instead, our programs are moving dollars mostly to relatively rich people who can afford to pay, and by offering forgiveness we are making the underlying problems far worse rather than better. Completely unacceptable even if it were constitutional. Colorado governor Jared Polis, who really ought to know better, signs bipartisan bill to make first two years of college free for students whose family income is under $90k/year at in-state public schools. Technically this is 65 credits not counting AP/IB, concurrent enrollment, military credit or credit for prior learning, so there is even more incentive to get such credits. The good news is they do have a full cliff, this falls off as you approach $90k, so they dodged the full version of quit-your-job insanity. The obvious bad news is that this is effectively one hell of a tax increase. The less obvious bad news is this is setting up a huge disaster. Think about what the student who actually needs this help will do. They will go to a local college for two years for free. If they do well, they'll get to 65 credits. Then the state will say 'oops, time to pay tuition.' And what happens now? Quite a lot of them will choose to, or be forced to, leave college and get a job. This is a disaster for everyone. The benefits of college mostly accrue to those who finish. At least roughly 25% of your wage premium is the pure Sheepskin Effect for getting your degree. If you aren't going to finish and were a marginal student to begin with (hence the not finishing), you are better off not going, even for free. I do not think we should be in the business of providing universal free college. There are real costs involved, including the negative externalities involved in accelerating credentialism. However, if we do want to make this offer to help people not drown, we need to at least not stop it halfway across the stream. What Your Tuition Buys You The real life version of the college where there degree students who pay for a degree but aren't allowed to come to class versus the non-degree students who get no degree but are educated for free. To be clear, this is totally awesome. David Weekly: This seems kinda…radical? ASU makes its courses available to anyone for $25/course. After you take the class, if you want the grade you got added to an official transcript with a credit you can use, +$400. These are real college credits. 8 year olds are getting college credits! Emmett Shear: This is cool to me because you can see the core of university economics right there. Bundling $25 worth of education with $400 of credentialist gatekeeping. I'm not blaming ASU, it's cool they're doing this, but that is deeply broken. Sudowoodo: Totally understand y...