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: AI for Bio: State Of The Field, published by sarahconstantin on August 31, 2024 on LessWrong. AI for biotech, particularly with drug discovery applications, has been used for more than a decade, with ambiguous success. But in the era of foundation models we may have experienced a step change in what's possible. I used to work on AI-for-drug-discovery years ago, at Recursion, where we sought to identify phenotypes of genetic diseases visible in microscopic images of cells, and screen for drugs that made the cells visually "look healthy" in the hopes that those drugs would also turn out to be effective against the symptoms of the disease. Circa 2016, we were just beginning to transition from the old-fashioned sort of machine learning based heavily on feature engineering, to the new "deep learning" paradigm with much larger neural nets. "Old-school" machine learning was often accused of being nothing more than logistic regression in fancy VC-funded branding, and there was often some truth to that. When our models worked best, they were picking up human-interpretable phenotypes that a pathologist could probably have described decades ago: something like "this disease causes enlarged nuclei". And, when we first started replacing the old models with deep neural nets, it wasn't clear that the New Hotness was going to work better than the Old Standby. But things have changed. Bigger, better models (often Transformer-based) are everywhere in biotech. They genuinely seem to be changing the state of the art in drug (and biologic) development. And it's past time to do a serious review of what's become available and what it can and can't do. AI optimists who aren't familiar with biotech are often wildly miscalibrated about what AI tools can do even in the best case scenario. The average approved drug in the US costs $879.3 million[1] in R&D expenses (counting the costs of failed drugs), and nearly 90% of that is spent on clinical trials. It's legally, scientifically, and ethically necessary to test drugs on humans to see if they're safe and effective. And while the ballooning cost of running clinical trials is a problem worth tackling in itself[2], it's inherently time- and labor-intensive to run valid experiments on human patients. An AI is never going to "design a drug" that you can give to patients right away. Even if the AI were a perfect all-knowing oracle, pharmaceutical companies would still need to run animal and then human trials. AI for biotech is attempting to automate and improve particular sub-problems within that 10% of costs spent on drug discovery and preclinical research. This is hardly trivial, especially if it enables the development of new classes of drugs that were completely inaccessible before. But it does place AI hype in context. An AI model's value to the drug discovery process is bounded by: the labor cost of the time it saves on more manual processes the cost it saves on any experiments it can fully replace the cost of any failed experiments it can prevent from being done altogether the value of any new successful therapies that would not even have been attempted without the model If the model tells you to do something you would probably have done anyway, it's useless. If the model replaces something you would have needed to do manually, it's somewhat useful. If the model increases your odds of a successful therapy, it's extremely useful, and if it adds successful therapies it's world-changing. With that paradigm set up, let's dig into the details. This won't be an exhaustive list of models, or an in-depth evaluation of their performance, but an overview of the big, influential, and buzzy and a summary of what they do. Structure Prediction Models One class of AI models with biotech applications tackles one of the most classically fiendish problems in c...

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: Principles for the AGI Race, published by William S on August 30, 2024 on LessWrong. Crossposted from https://williamrsaunders.substack.com/p/principles-for-the-agi-race Why form principles for the AGI Race? I worked at OpenAI for 3 years, on the Alignment and Superalignment teams. Our goal was to prepare for the possibility that OpenAI succeeded in its stated mission of building AGI (Artificial General Intelligence, roughly able to do most things a human can do), and then proceed on to make systems smarter than most humans. This will predictably face novel problems in controlling and shaping systems smarter than their supervisors and creators, which we don't currently know how to solve. It's not clear when this will happen, but a number of people would throw around estimates of this happening within a few years. While there, I would sometimes dream about what would have happened if I'd been a nuclear physicist in the 1940s. I do think that many of the kind of people who get involved in the effective altruism movement would have joined, naive but clever technologists worried about the consequences of a dangerous new technology. Maybe I would have followed them, and joined the Manhattan Project with the goal of preventing a world where Hitler could threaten the world with a new magnitude of destructive power. The nightmare is that I would have watched the fallout of bombings of Hiroshima and Nagasaki with a growing gnawing panicked horror in the pit of my stomach, knowing that I had some small share of the responsibility. Maybe, like Albert Einstein, I would have been unable to join the project due to a history of pacifism. If I had joined, I like to think that I would have joined the ranks of Joseph Rotblat and resigned once it became clear that Hitler would not get the Atomic Bomb. Or joined the signatories of the Szilárd petition requesting that the bomb only be used after terms of surrender had been publicly offered to Japan. Maybe I would have done something to try to wake up before the finale of the nightmare. I don't know what I would have done in a different time and place, facing different threats to the world. But as I've found myself entangled in the ongoing race to build AGI, it feels important to reflect on the lessons to learn from history. I can imagine this alter ego of myself and try to reflect on how I could take right actions in both this counterfactual world and the one I find myself in now. In particular, what could guide me to the right path even when I'm biased, subtly influenced by the people around me, misinformed, or deliberately manipulated? Simply trying to pick the action you think will lead to the best consequences for the world fails to capture the ways in which your model of the world is wrong, or your own thinking is corrupt. Joining the Manhattan Project, and using the weapons on Japan both had plausible consequentialist arguments supporting them, ostensibly inviting a lesser horror into the world to prevent a greater one. Instead I think the best guiding star to follow is reflecting on principles, rules which apply in a variety of possible worlds, including worlds in which you are wrong. Principles that help you gather the right information about the world. Principles that limit the downsides if you're wrong. Principles that help you tell whether you're in a world where racing to build a dangerous technology first is the best path, or you're in a world where it's a hubristic self-delusion. This matches more with the idea of rule consequentialism than pure act consequentialism: instead of making each decision based on what you think is best, think about what rules would be good for people to adopt if they were in a similar situation. My goal in imagining these principles is to find principles that prevent errors of the following forms...

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: Should you work at a frontier AI company?, published by 80000 Hours on August 30, 2024 on The Effective Altruism Forum. This is a cross-post of our newly updated article with advice on working at frontier AI companies if you want to help with AI safety. The original article was published in June 2023. We decided to update it in May 2024, as events in the intervening year caused us to be more concerned about working for frontier AI companies, and we also wanted to explain why we still thought it made sense for some people to take roles at AI companies. Our overall view is that "it's complicated": working at a frontier AI company is sometimes a good idea, and sometimes a bad idea; it depends on the role and type of work, the company, and your individual case. The new article tries to more clearly lay out the case in favour and against working at frontier AI companies, give tips for mitigating the downsides, and give more information on alternatives. Summary In a nutshell: If you want to help reduce catastrophic risks from AI, working at a frontier AI company is an important option to consider, but the impact is hard to assess. These roles often come with great potential for career growth, and many could be (or lead to) highly impactful ways of reducing the chances of an AI-related catastrophe. However, there's also a risk of doing substantial harm, and there are roles you should probably avoid. Pros Some roles have high potential for a big positive impact via reducing risks from AI Among the best and most robust ways to gain AI-specific career capital Highly compensated and prestigious Cons Risk of contributing to or accelerating AI systems that could cause extreme harm Financial and social incentives might make it harder to think objectively about risks Stress, especially because of a need to carefully and repeatedly assess whether your role is harmful Recommendation: it's complicated We think there are people in our audience for whom working in a role at a frontier AI company is their highest-impact option. But some of these roles might also be extremely harmful. This means it's important to be discerning and vigilant when thinking about taking a role at a frontier AI company - both about the role and the company overall - and to actually be willing to leave if you end up thinking the work is harmful. Review status: Based on an in-depth investigation This review is informed by three surveys of people we regard as having expertise: one survey on whether you should be open to roles that advance AI capabilities (written up here), and two followup surveys conducted over the past two years. It's likely there are still gaps in our understanding, as many of these considerations remain highly debated. The domain is also fast moving, so this article may become outdated quickly. This article was originally published in June 2023. It was substantially updated in August 2024 to reflect more recent developments and thinking. Introduction We think AI is likely to have transformative effects over the coming decades, and that reducing the chances of an AI-related catastrophe is one of the world's most pressing problems. So it's natural to wonder whether you should try to work at one of the companies that are doing the most to build and shape these future AI systems. As of summer 2024, OpenAI, Google DeepMind, Meta, and Anthropic seem to be the leading frontier AI companies - meaning they have produced the most capable models so far and seem likely to continue doing so. Mistral, and xAI are contenders as well - and others may enter the industry from here.1 Why might it be high impact to work for a frontier AI company? Some roles at these companies might be among the best for reducing risks We suggest working at frontier AI companies in several of our career reviews because a lot of 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: Singular learning theory: exercises, published by Zach Furman on August 30, 2024 on LessWrong. Thanks to Jesse Hoogland and George Wang for feedback on these exercises. In learning singular learning theory (SLT), I found it was often much easier to understand by working through examples, rather than try to work through the (fairly technical) theorems in their full generality. These exercises are an attempt to collect the sorts of examples that I worked through to understand SLT. Before doing these exercises, you should have read the Distilling Singular Learning Theory (DSLT) sequence, watched the SLT summit YouTube videos, or studied something equivalent. DSLT is a good reference to keep open while solving these problems, perhaps alongside Watanabe's textbook, the Gray Book. Note that some of these exercises cover the basics, which are well-covered in the above distillations, but some deliver material which will likely be new to you (because it's buried deep in a textbook, because it's only found in adjacent literature, etc). Exercises are presented mostly in conceptual order: later exercises freely use concepts developed in earlier exercises. Starred (*) exercises are what I consider the most essential exercises, and the ones I recommend you complete first. 1. *The normal distribution, like most classical statistical models, is a regular (i.e. non-singular[1]) statistical model. A univariate normal model with unit variance and mean μR is given by the probability density p(x|μ)=12πexp(12(xμ)2). Assume the true distribution q(x) of the data is realizable by the model: that is, q(x)=p(x|μ0) for some true parameter μ0. 1. Calculate the Fisher information matrix of this model (note that since we have only a single parameter, the FIM will be a 1x1 matrix). Use this to show the model is regular. 2. Write an explicit expression for the KL divergence K(μ) between q(x) and p(x|μ), as a function of the parameter μ. This quantity is sometimes also called the population loss. [See Example 1.1, Gray Book, for the case of a 2D normal distribution] 3. Using K(μ) from b), give an explicit formula for the volume of "almost optimal" parameters, V(ϵ)={μK(μ) 4. The volume scaling formula for the learning coefficient λ (also known as RLCT[2]) is λ=limϵ0log(V(aϵ)/V(ϵ))log(a) for any a1 [Theorem 7.1, Gray Book]. Using this formula, combined with the expression for V(ϵ) derived in b), calculate the learning coefficient[3]. Given that the model is regular, we expect the learning coefficient to be d2=12; compare your answer. 2. *We can make the normal distribution a singular model by changing the parameterization. Let a cubicly-parameterized normal model be the model p(x|μ)=12πexp(12(xμ3)2). Assume the true parameter is μ0. 1. Show that the cubicly-parameterized normal model is just as expressive as an ordinary normal model: that is, they both can express all univariate normal distributions. 2. Repeat 1a) with this model; calculate the Fisher information matrix to demonstrate that the model is singular, and find which parameters μ are singular. 3. Repeat 1b) - 1d) to calculate the learning coefficient this model, for μ0=0, and for μ00. Recall that the learning coefficient is a volume scaling exponent, such that V(ϵ)ϵλ [4] as ϵ0. Based on this, interpret your results. How does this make the cubicly-parameterized normal model different from the ordinary normal model? 4. Instead of taking ϵ0 to get the learning coefficient, fix a small but nonzero value for ϵ, such as ϵ=0.01. As we saw from c), the learning coefficient changes discontinuously when μ0=0 - what happens with V(ϵ) as μ0 gets close to zero? What changes if you make ϵ smaller or larger? Even though the asymptotic learning coefficien...

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: AIS terminology proposal: standardize terms for probability ranges, published by Egg Syntax on August 30, 2024 on The AI Alignment Forum. Summary: The AI safety research community should adopt standardized terms for probability ranges, especially in public-facing communication and especially when discussing risk estimates. The terms used by the IPCC are a reasonable default. Science communication is notoriously hard. It's hard for a lot of reasons, but one is that laypeople aren't used to thinking in numerical probabilities or probability ranges. One field that's had to deal with this more than most is climatology; climate change has been rather controversial, and a non-trivial aspect of that has been lay confusion about what climatologists are actually saying[1]. As a result, the well-known climate assessment reports from the UN's Intergovernmental Panel on Climate Change (IPCC) have, since the 1990s, used explicitly defined terms for probability ranges[2]: (see below for full figure[3]) Like climatology, AI safety research has become a topic of controversy. In both cases, the controversy includes a mix of genuine scientific disagreement, good-faith confusion, and bad-faith opposition. Scientific disagreement comes from people who can deal with numerical probability ranges. Those who are arguing in bad faith from ulterior motives generally don't care about factual details. But I suspect that the large majority of those who disagree, especially laypeople, are coming from a place of genuine, good-faith confusion. For those people, anything we as practitioners can do to communicate more clearly is quite valuable. Also like climatology, AI safety research, especially assessments of risk, fundamentally involves communicating about probabilities and probability ranges. Therefore I propose that the AIS community follow climatologists in adopting standard terms for probability ranges, especially in position papers and public-facing communication. In less formal and less public-facing contexts, using standard terminology still adds some value but is less important; in sufficiently informal contexts it's probably not worth the hassle of looking up the standard terminology. Of course, in many cases it's better to just give the actual numerical range! But especially in public-facing communication it can be more natural to use natural language terms, and in fact this is already often done. I'm only proposing that when we do use natural language terms for probability ranges, we use them in a consistent and interpretable way (feel free to link to this post as a reference for interpretation, or point to the climatology papers cited below[2]). Should the AIS community use the same terms? That's a slightly harder question. The obvious first-pass answer is 'yes'; it's a natural Schelling point, and terminological consistency across fields is generally preferable when practically possible. The IPCC terms also have the significant advantage of being battle-tested; they've been used over a thirty-year period in a highly controversial field, and terms have been refined when they were found to be insufficiently clear. The strongest argument I see against using the same terms is that the AIS community sometimes needs to deal with more extreme (high or low) risk estimates than these. If we use 'virtually certain' to mean 99 - 100%, what terms can we use for 99.9 - 100.0%, or 99.99 - 100.00%? On the other hand, plausibly once we're dealing with such extreme risk estimates, it's increasingly important to communicate them with actual numeric ranges. My initial proposal is to adopt the IPCC terms, but I'm very open to feedback, and if someone has an argument I find compelling (or which gets strong agreement in votes) for a different or extended set of terms, I'll add it to the proposal. If no su...

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: Nursing doubts, published by dynomight on August 30, 2024 on LessWrong. If you ask the internet if breastfeeding is good, you will soon learn that YOU MUST BREASTFEED because BREAST MILK = OPTIMAL FOOD FOR BABY. But if you look for evidence, you'll discover two disturbing facts. First, there's no consensus about why breastfeeding is good. I've seen experts suggest at least eight possible mechanisms: 1. Formula can't fully reproduce the complex blend of fats, proteins and sugars in breast milk. 2. Formula lacks various bio-active things in breast milk, like antibodies, white blood cells, oligosaccharides, and epidermal growth factor. 3. If local water is unhealthy, then the mother's body acts as a kind of "filter". 4. Breastfeeding may have psychological/social benefits, perhaps in part by releasing oxytocin in the mother. 5. Breastfeeding decreases fertility, meaning the baby may get more time before resources are redirected to a younger sibling. 6. Breastfeeding may help mothers manage various post-birth health issues? 7. Infants are often given formula while lying on their backs, which might lead to fluid buildup in the ears and thus temporary hearing loss during a critical development period? 8. Breastfeeding is cheaper?? Second, the evidence for breastfeeding is overwhelmingly observational: It's not based on experiments, but rather looking at the existing population and "observing" that breastfeeding is correlated with having mildly fewer infections (of many kinds) and slightly lower obesity. It may also be correlated with better outcomes in terms of allergies, diabetes, lymphoma, colitis, Crohn's disease, or later IQ. Observational evidence is disturbing because correlations are bad. Even if breastfeeding did nothing, people think it's good, so the same parents who breastfeed more tend to have higher socioeconomic status and provide lots of other goodies too. Babies that wear baby Rolex watches are probably healthier on average. But that's because their parents are rich, not because Rolexes are good for you. Could breastfeeding be like that? Of course, experts are aware of this issue. They try to compensate for it by "controlling" for upstream variables. The most-cited meta-analysis on breastfeeding and IQ collected 18 papers that each controlled for different things, like parental education, social status, or how much social interaction the baby got. The control variables seemed to matter a lot: Among studies that… Breastfeeding associated with a… Did not control for maternal IQ 4.1 IQ point increase Controlled for maternal IQ 2.6 IQ point increase But what about paternal IQ? Might smarter dads convince mothers to breastfeed more? What if you forgot to control for something, or your data was noisy, or the relationship is nonlinear? (What if smarter babies manipulate their mothers into breastfeeding more?) If any of that happens, then correlations will probably exaggerate the causal impact of breastfeeding. So there's been a small movement in recent years to push back against Big Nurse, to argue that, despite the public health messaging, there is no clear evidence that breastfeeding is beneficial. (See Stuart Richie at Science Fictions or Emily Oster at Five Thirty Eight or The Guardian for good versions of this argument.) Naturally, I am sympathetic. Down with groupthink! Down with control variables! Down with putting so much pressure on mothers based on weak evidence! Except… Imagine you just gave birth on a desert island - one that for some reason has an unlimited supply of formula. You're considering breastfeeding your baby, but you can't read any studies. What should you do? Well, there's an obvious evolutionary argument. Maybe the epidermal growth factor and obscure mix of fats in breast milk are crucial. Or maybe they aren't. But they're probably not bad...

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: Things I learned talking to the new breed of scientific institution, published by Abhishaike Mahajan on August 30, 2024 on LessWrong. Note: this article is sponsored by and cross-posted to the Good Science Project. They also write a fair bit, and their articles were essential reading for writing this essay! Also, this article would not be possible without the hours of discussion/editing help I've had with several people from these institutions, and a few outside of them. Huge shout-out to all of them! Introduction Arcadia Science, Speculative Technologies, FutureHouse, Arc, and Convergent. All of these are a new form of scientific institute. Most are funded entirely by a few billionaires. Most are non-profits. Most of them focus on the life-sciences. Most of them have sprung up in just the last few years. They do all also have one common thread: a grand statement. We are an experiment in a new way to do science. And they are! Traditionally, research is conducted in academic or private industry labs - dependent on NIH grants in the former and markets in the latter. Given the (often singular) sources of no-strings-attached funding, these new institutions need not satisfy either the NIH or the markets, allowing them to conduct research in a unique fashion. In one sense, the experimental aspect of these institutions revolves around the focus of the research itself, addressing fields or using methods that the founders - correctly or not - view as underserved/underutilized. But, on a more subtle level, the experimental aspect could be more closely tied to the culture of these organizations. Institutions like Arcadia, FutureHouse, and the rest could be viewed as the production of auteurs - a term from filmmaking for films with such a heavy sense of the director's personal taste that the film is inseparable from the director. This is where the novelty within these institutions primarily lie, in how the founders of the institute wish science was conducted. And wielding billions of dollars, thousands of hours of work, and hundreds of scientists as a means to test whether their theories are correct. Of course, nothing under the sun is truly new. There is an age-old history of scientist dissatisfaction with how 'things are traditionally done', and confidently building new institutions to solve the problems they've seen. Many of these are now household names amongst researchers: Broad Institute, Whitehead Institute, Max Planck Society, Howard Hughes Medical Institute (HHMI), and so on. Each of these were started with similar contrarian mentalities as the current era of institutions. Some of these were more experimental than others, most notably HHMI, which prized itself on its focus on interdisciplinary research above all else. But all were experiments, many of them extraordinarily successful. Yet, the current iteration of new research institutes is still arguably more experimental than its ancestors. While the last generation of institutes was typically tied directly to universities, the current era of ones (outside of Arc) are independent, allowing them a larger sense of opinionation on how science should be done. But, despite this experimentation, there is relatively little information out there on what's going on inside them. Not in terms of science, but more-so the vibes. While aspects of these organizations have been written about previously, such as in articles in The Atlantic and Endpoints, they aren't assessing vibes! These other articles are, first and foremost, news-pieces; valuable, but lack any opinionated observations on the inner-workings of the institutions. Nadia Asparouhova's essay on the subject comes closest to this regarding the history of these institutions, but still few details on how they practically function. This essay attempts to discuss that missing s...

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: Solving adversarial attacks in computer vision as a baby version of general AI alignment, published by stanislavfort on August 30, 2024 on LessWrong. I spent the last few months trying to tackle the problem of adversarial attacks in computer vision from the ground up. The results of this effort are written up in our new paper Ensemble everything everywhere: Multi-scale aggregation for adversarial robustness (explainer on X/Twitter). Taking inspiration from biology, we reached state-of-the-art or above state-of-the-art robustness at 100x - 1000x less compute, got human-understandable interpretability for free, turned classifiers into generators, and designed transferable adversarial attacks on closed-source (v)LLMs such as GPT-4 or Claude 3. I strongly believe that there is a compelling case for devoting serious attention to solving the problem of adversarial robustness in computer vision, and I try to draw an analogy to the alignment of general AI systems here. 1. Introduction In this post, I argue that the problem of adversarial attacks in computer vision is in many ways analogous to the larger task of general AI alignment. In both cases, we are trying to faithfully convey an implicit function locked within the human brain to a machine, and we do so extremely successfully on average. Under static evaluations, the human and machine functions match up exceptionally well. However, as is typical in high-dimensional spaces, some phenomena can be relatively rare and basically impossible to find by chance, yet ubiquitous in their absolute count. This is the case for adversarial attacks - imperceptible modifications to images that completely fool computer vision systems and yet have virtually no effect on humans. Their existence highlights a crucial and catastrophic mismatch between the implicit human vision function and the function learned by machines - a mismatch that can be exploited in a dynamic evaluation by an active, malicious agent. Such failure modes will likely be present in more general AI systems, and our inability to remedy them even in the more restricted vision context (yet) does not bode well for the broader alignment project. This is a call to action to solve the problem of adversarial vision attacks - a stepping stone on the path to aligning general AI systems. 2. Communicating implicit human functions to machines The basic goal of computer vision can be viewed as trying to endow a machine with the same vision capabilities a human has. A human carries, locked inside their skull, an implicit vision function mapping visual inputs into semantically meaningful symbols, e.g. a picture of a tortoise into a semantic label tortoise. This function is represented implicitly and while we are extremely good at using it, we do not have direct, conscious access to its inner workings and therefore cannot communicate it to others easily. To convey this function to a machine, we usually form a dataset of fixed images and their associated labels. We then use a general enough class of functions, typically deep neural networks, and a gradient-based learning algorithm together with backpropagation to teach the machine how to correlate images with their semantic content, e.g. how to assign a label parrot to a picture of a parrot. This process is extremely successful in communicating the implicit human vision function to the computer, and the implicit human and explicit, learned machine functions agree to a large extent. The agreement between the two is striking. Given how different the architectures are (a simulated graph-like function doing a single forward pass vs the wet protein brain of a mammal running continuous inference), how different the learning algorithms are (gradient descent with backpropagation vs something completely different but still unknown), and how differ...

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: #198 - Challenging our assumptions about insects (Meghan Barrett on The 80,000 Hours Podcast), published by 80000 Hours on August 29, 2024 on The Effective Altruism Forum. We just published an interview: Meghan Barrett on challenging our assumptions about insects. Listen on Spotify, watch on Youtube, or click through for other audio options, the transcript, and related links. Below are the episode summary and some key excerpts. Episode summary This is a group of animals I think people are particularly unfamiliar with. They are especially poorly covered in our science curriculum; they are especially poorly understood, because people don't spend as much time learning about them at museums; and they're just harder to spend time with in a lot of ways, I think, for people. So people have pets that are vertebrates that they take care of across the taxonomic groups, and people get familiar with those from going to zoos and watching their behaviours there, and watching nature documentaries and more. But I think the insects are still really underappreciated, and that means that our intuitions are probably more likely to be wrong than with those other groups. Meghan Barrett In today's episode, host Luisa Rodriguez speaks to Meghan Barrett - insect neurobiologist and physiologist at Indiana University Indianapolis and founding director of the Insect Welfare Research Society - about her work to understand insects' potential capacity for suffering, and what that might mean for how humans currently farm and use insects. They cover: The scale of potential insect suffering in the wild, on farms, and in labs. Examples from cutting-edge insect research, like how depression- and anxiety-like states can be induced in fruit flies and successfully treated with human antidepressants. How size bias might help explain why many people assume insects can't feel pain. Practical solutions that Meghan's team is working on to improve farmed insect welfare, such as standard operating procedures for more humane slaughter methods. Challenges facing the nascent field of insect welfare research, and where the main research gaps are. Meghan's personal story of how she went from being sceptical of insect pain to working as an insect welfare scientist, and her advice for others who want to improve the lives of insects. And much more. Producer and editor: Keiran Harris Audio engineering by Ben Cordell, Milo McGuire, Simon Monsour, and Dominic Armstrong Additional content editing: Katy Moore and Luisa Rodriguez Transcriptions: Katy Moore Highlights Size diversity Meghan Barrett: You've seen a lot of insects; you've probably mostly seen small ones. But it turns out that this is not characteristic of the entire group. Insect species vary by a factor of about 5.2 million in body mass from the smallest to the largest. So to give you some context for that, from a vertebrate perspective, birds are our flying terrestrial vertebrates; insects are our flying terrestrial invertebrates. Birds only vary by a factor of about 72,000 in body mass, whereas in insects, it's 5.2 million. And so we've got really, really, really tiny insects like parasitic wasps and featherwing beetles that are super, super small - like, on the scale of some cells, single-cell organisms: they're the same size as those, which just also goes to show you how big those can even get. Life is very diverse. Then we've got these gigantic beetles that if you were to put one on the palm of an adult's hand, would cover from the palm all the way to the fingertips in the longhorn beetles. We've got giant Goliath beetles, we've got huge stick insects - just really, really big insects are also a thing that we have, and people just tend to be less familiar with them. One example I like to give, again from a vertebrate perspective, where I think we're more fam...

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: AI #79: Ready for Some Football, published by Zvi on August 29, 2024 on LessWrong. I have never been more ready for Some Football. Have I learned all about the teams and players in detail? No, I have been rather busy, and have not had the opportunity to do that, although I eagerly await Seth Burn's Football Preview. I'll have to do that part on the fly. But oh my would a change of pace and chance to relax be welcome. It is time. The debate over SB 1047 has been dominating for weeks. I've now said my peace on the bill and how it works, and compiled the reactions in support and opposition. There are two small orders of business left for the weekly. One is the absurd Chamber of Commerce 'poll' that is the equivalent of a pollster asking if you support John Smith, who recently killed your dog and who opponents say will likely kill again, while hoping you fail to notice you never had a dog. The other is a (hopefully last) illustration that those who obsess highly disingenuously over funding sources for safety advocates are, themselves, deeply conflicted by their funding sources. It is remarkable how consistently so many cynical self-interested actors project their own motives and morality onto others. The bill has passed the Assembly and now it is up to Gavin Newsom, where the odds are roughly 50/50. I sincerely hope that is a wrap on all that, at least this time out, and I have set my bar for further comment much higher going forward. Newsom might also sign various other AI bills. Otherwise, it was a fun and hopeful week. We saw a lot of Mundane Utility, Gemini updates, OpenAI and Anthropic made an advance review deal with the American AISI and The Economist pointing out China is non-zero amounts of safety pilled. I have another hopeful iron in the fire as well, although that likely will take a few weeks. And for those who aren't into football? I've also been enjoying Nate Silver's On the Edge. So far, I can report that the first section on gambling is, from what I know, both fun and remarkably accurate. Table of Contents 1. Introduction. 2. Table of Contents. 3. Language Models Offer Mundane Utility. Turns out you did have a dog. Once. 4. Language Models Don't Offer Mundane Utility. The AI did my homework. 5. Fun With Image Generation. Too much fun. We are DOOMed. 6. Deepfaketown and Botpocalypse Soon. The removal of trivial frictions. 7. They Took Our Jobs. Find a different job before that happens. Until you can't. 8. Get Involved. DARPA, Dwarkesh Patel, EU AI Office. Last two in SF. 9. Introducing. Gemini upgrades, prompt engineering guide, jailbreak contest. 10. Testing, Testing. OpenAI and Anthropic formalize a deal with the US's AISI. 11. In Other AI News. What matters? Is the moment over? 12. Quiet Speculations. So many seem unable to think ahead even mundanely. 13. SB 1047: Remember. Let's tally up the votes. Also the poll descriptions. 14. The Week in Audio. Confused people bite bullets. 15. Rhetorical Innovation. Human preferences are weird, yo. 16. Aligning a Smarter Than Human Intelligence is Difficult. 'Alignment research'? 17. People Are Worried About AI Killing Everyone. The Chinese, perhaps? 18. The Lighter Side. Got nothing for you. Grab your torches. Head back to camp. Language Models Offer Mundane Utility Chat with Scott Sumner's The Money Illusion GPT about economics, with the appropriate name ChatTMI. It's not perfect, but he says it's not bad either. Also, did you know he's going to Substack soon? Build a nuclear fusor in your bedroom with zero hardware knowledge, wait what? To be fair, a bunch of humans teaching various skills and avoiding electrocution were also involved, but still pretty cool. Import things automatically to your calendar, generalize this it seems great. Mike Knoop (Co-founder Zapier and Arc Prize): Parent tip: you can upload a ph...