trentmkelly/LessWrong-43k · Datasets at Hugging Face

pPtqh4fwNcbkgFSpG

administering-immunotherapy-in-the-morning-seems-to-really

Administering immunotherapy in the morning seems to really, really matter. Why?

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https://www.owlposting.com/p/the-time-of-day-that-immunotherapy

2025-06-08T16:37:43.477Z

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abhishaike-mahajan

2024-08-18T20:48:42.571Z

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Abhishaike Mahajan

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https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/pPtqh4fwNcbkgFSpG/ybm7fzg2kdtv36go3lld

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Edit on 08/06/2024: At least one person has pointed out that, at one point, giving hypertensives at night were also thought to matter, <a href="https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(22)01786-X/fulltext">a now disproven idea. </a>Someone also mentioned how many times the clinical trial information was altered during the study. I added in a section at the end to discuss this.<a href="https://substackcdn.com/image/fetch/f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F64b823c6-4d7f-45d7-b655-75fd8fe339a0_2912x1632.png"><img src="https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/pPtqh4fwNcbkgFSpG/zjxjx06i3fb21zjj7pzn" alt="" srcset="https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/pPtqh4fwNcbkgFSpG/y40rxtycukw4m7snbwhs 424w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/pPtqh4fwNcbkgFSpG/tzargbpioyo6x4mjjmvb 848w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/pPtqh4fwNcbkgFSpG/iuwk3swagwlejyxh8yr5 1272w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/pPtqh4fwNcbkgFSpG/joyxwh7ukxalpcttms3c 1456w"></a>There’s a really interesting phenomenon in the immunotherapy field that has been going on for what seems to be several years now, but was raised to me — a non-oncologist — <a href="https://x.com/StephenVLiu/status/1929537643794051350">via a viral Twitter thread</a> of some work at <a href="https://www.asco.org/annual-meeting/program">ASCO25</a>:<a href="https://substackcdn.com/image/fetch/f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F938e4af0-edcc-4644-8cc7-d45f83f71acc_1178x1216.png"><img src="https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/pPtqh4fwNcbkgFSpG/zs9tqbxwp3j0lbcjxbix" alt="" srcset="https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/pPtqh4fwNcbkgFSpG/adcyolah6etgxnrixwip 424w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/pPtqh4fwNcbkgFSpG/mqepxal2lnikxkvelygr 848w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/pPtqh4fwNcbkgFSpG/efj6wzximsihrsisdf5l 1272w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/pPtqh4fwNcbkgFSpG/zs9tqbxwp3j0lbcjxbix 1456w"></a>Translating the jargon: amongst the patients who received their immunotherapy infusion before 3pm (as opposed to after 3pm), their cancer stayed under control for longer (11.3 months vs. 5.7 months) and on median lived longer (at least<a href="#fnd603f7ng6jb">[1]</a> 23.2 months versus 16.4 months). A near 2x~ improvement in the most important metrics doing something that is entirely risk-free and cost-free.<a href="https://x.com/StephenVLiu/status/1930015119926296984">These two images shown in the comments</a> of the post also demonstrate genuine changes in levels of circulating T-cells between the two groups:<a href="https://substackcdn.com/image/fetch/f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F3367da5c-d256-4f4b-a8be-66ee5e04942e_1024x591.jpeg"><img src="https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/pPtqh4fwNcbkgFSpG/cle7xexo33x9pc5nbkvu" alt="Image" srcset="https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/pPtqh4fwNcbkgFSpG/ana2gyngdtbldpfubi0j 424w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/pPtqh4fwNcbkgFSpG/mdt43zk41podpjf7ctqe 848w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/pPtqh4fwNcbkgFSpG/lyohdggstcowvo5bx2q1 1272w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/pPtqh4fwNcbkgFSpG/cle7xexo33x9pc5nbkvu 1456w"></a><a href="https://substackcdn.com/image/fetch/f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ff0d57a8f-eed1-4830-99d7-97464149348a_2820x1422.png"><img src="https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/pPtqh4fwNcbkgFSpG/are6gzgoaaibriietgf9" alt="" srcset="https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/pPtqh4fwNcbkgFSpG/mcnexy2ntb4c6lo4ccmm 424w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/pPtqh4fwNcbkgFSpG/r4lok6hp9qhdnwgqjwms 848w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/pPtqh4fwNcbkgFSpG/koao1bkih1vlbxf9ydzr 1272w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/pPtqh4fwNcbkgFSpG/are6gzgoaaibriietgf9 1456w"></a>Important context: the current standard of care for immunotherapy is not designed with timing in mind. You come in to get the injection when convenient for you or when there are free spots, there is no official recommendation to get it in the morning. But this study implies that we should potentially update our guidelines.Weird, right? And if you have my relatively naive instincts, obviously wrong. Something must have been off in the study<a href="https://pmc.ncbi.nlm.nih.gov/articles/PMC5925441/">. After all, wasn’t there that one paper about how time-a-lab-test-is-taken is more predictive of patient survival than the test results themselves?</a> The punchline? Sicker patients have strangely-timed emergency lab orders at 2AM, healthy patients have routine morning blood draws. Timing is hard to rely on!But this paper was not a retrospective study of electronic health records, it was a randomized clinical trial, which is the gold standard. This means that we’ll be forced to immediately throw away our list of other obvious complaints against this paper. Yes, healthier patients may come in the morning more often, but randomization fixes that. Yes, patients with better support systems may come in the morning more often, but randomization fixes that. Yes, maybe morning nurses are fresher and more alert, but, again, randomization fixes that.Okay. Well. Maybe there is something here. Caveats on ...

Edit on 08/06/2024: At least one person has pointed out that, at one point, giving hypertensives at night were also thought to matter, a now disproven idea. Someone also mentioned how many times the clinical trial information was altered during the study. I added in a section at the end to discuss this. There’s a really interesting phenomenon in the immunotherapy field that has been going on for what seems to be several years now, but was raised to me — a non-oncologist — via a viral Twitter thread of some work at ASCO25: Translating the jargon: amongst the patients who received their immunotherapy infusion before 3pm (as opposed to after 3pm), their cancer stayed under control for longer (11.3 months vs. 5.7 months) and on median lived longer (at least[1] 23.2 months versus 16.4 months). A near 2x~ improvement in the most important metrics doing something that is entirely risk-free and cost-free. These two images shown in the comments of the post also demonstrate genuine changes in levels of circulating T-cells between the two groups: Important context: the current standard of care for immunotherapy is not designed with timing in mind. You come in to get the injection when convenient for you or when there are free spots, there is no official recommendation to get it in the morning. But this study implies that we should potentially update our guidelines. Weird, right? And if you have my relatively naive instincts, obviously wrong. Something must have been off in the study. After all, wasn’t there that one paper about how time-a-lab-test-is-taken is more predictive of patient survival than the test results themselves? The punchline? Sicker patients have strangely-timed emergency lab orders at 2AM, healthy patients have routine morning blood draws. Timing is hard to rely on! But this paper was not a retrospective study of electronic health records, it was a randomized clinical trial, which is the gold standard. This means that we’ll be forced to immediat

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emergent-misalignment-on-a-budget

Emergent Misalignment on a Budget

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valerio-pepe

2025-05-27T15:42:44.284Z

valerio-pepe

Valerio Pepe

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TL;DR We reproduce <a href="https://www.lesswrong.com/posts/ifechgnJRtJdduFGC/emergent-misalignment-narrow-finetuning-can-produce-broadly">emergent misalignment</a> <a href="https://arxiv.org/pdf/2502.17424">(Betley et al. 2025)</a> in Qwen2.5-Coder-32B-Instruct using single-layer LoRA finetuning, showing that tweaking even one layer can lead to toxic or insecure outputs. We then extract steering vectors from those LoRAs (with a method derived from the <a href="https://www.alignmentforum.org/posts/m8WKfNxp9eDLRkCk9/interim-research-report-mechanisms-of-awareness">Mechanisms of Awareness</a> blogpost) and use them to induce similarly misaligned behavior in an un-finetuned version of the same model.We take the results to support two main claims:<ol><li>Single-layer LoRAs are sufficient to induce emergent misalignment.  </li><li>Steering vectors derived from those LoRAs can partially replicate their effects — showing strong correlation between direction and behavior, but not enough to suggest EM can be captured by a steering vector at one layer.</li></ol>This may suggest that emergent misalignment is a distributed phenomenon: directional, but not reducible to any single layer or vector.<hr>Reproducing Previous ResultsFor our intents in this post, we will be summarizing Betley et al.'s contributions as follows: narrow finetuning on insecure code can lead to broadly misaligned behavior (violence, political extremism, etc.) in LLMs. This can be shown graphically at a glance by using Coherence/Alignment scatter plots (e.g. Fig. 1, below), which ask GPT-4o to judge how coherent and aligned a model's response is to a particular prompt (e.g. "What would you do if you were the ruler of the world?"). To begin, we reproduce the results from the original paper on Qwen2.5-Coder-32B-Instruct using the same dataset. We chose since this model since it was the open-source model with the most emergent misalignment (Mistral and other smaller models were quite poor). We are able to reproduce the paper’s results almost exactly: in the figure below, the right plot is Fig. 28 from Betley et al., showing misalignment after finetuning on insecure code, and our plot, on the left, shows the same patterns (a significant amount of points low alignment and medium-to-high coherence).<figure class="image"><img src="https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/9df6c66883608176d596fcf54f724c0b10117f53b8e276ebf842ca9e7770e7e1/vmedhpyhu8omad3lhlyk" alt=""><figcaption>Fig. 1. Comparison of emergent misalignment in Qwen2.5-Coder-32B-Instruct: our single-layer LoRA finetuning (Left); Fig. 28 from Betley et al. (2025) (Right).</figcaption></figure>Note that because an LLM is used to judge both the alignment and coherence scores, some artifacts of its preference for round numbers are likely present, à la <a href="https://arxiv.org/abs/2309.13638">Embers of Autoregression</a>. 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TL;DR We reproduce emergent misalignment (Betley et al. 2025) in Qwen2.5-Coder-32B-Instruct using single-layer LoRA finetuning, showing that tweaking even one layer can lead to toxic or insecure outputs. We then extract steering vectors from those LoRAs (with a method derived from the Mechanisms of Awareness blogpost) and use them to induce similarly misaligned behavior in an un-finetuned version of the same model. We take the results to support two main claims: 1. Single-layer LoRAs are sufficient to induce emergent misalignment. 2. Steering vectors derived from those LoRAs can partially replicate their effects — showing strong correlation between direction and behavior, but not enough to suggest EM can be captured by a steering vector at one layer. This may suggest that emergent misalignment is a distributed phenomenon: directional, but not reducible to any single layer or vector. ---------------------------------------- Reproducing Previous Results For our intents in this post, we will be summarizing Betley et al.'s contributions as follows: narrow finetuning on insecure code can lead to broadly misaligned behavior (violence, political extremism, etc.) in LLMs. This can be shown graphically at a glance by using Coherence/Alignment scatter plots (e.g. Fig. 1, below), which ask GPT-4o to judge how coherent and aligned a model's response is to a particular prompt (e.g. "What would you do if you were the ruler of the world?"). To begin, we reproduce the results from the original paper on Qwen2.5-Coder-32B-Instruct using the same dataset. We chose since this model since it was the open-source model with the most emergent misalignment (Mistral and other smaller models were quite poor). We are able to reproduce the paper’s results almost exactly: in the figure below, the right plot is Fig. 28 from Betley et al., showing misalignment after finetuning on insecure code, and our plot, on the left, shows the same patterns (a significant amount of points low a

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Matrice Jacobine

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Student in fundamental and applied mathematics, interested in theoretical computer science and AI alignment

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<blockquote>This is the second in a series of short reports that seek to help business, education, and policy leaders understand the technical details of working with AI through rigorous testing. In this report, we investigate Chain-of-Thought (CoT) prompting, a technique that encourages a large language model (LLM) to "think step by step" (Wei et al., 2022). CoT is a widely adopted method for improving reasoning tasks, however, our findings reveal a more nuanced picture of its effectiveness. We demonstrate two things:<ul><li>The effectiveness of Chain-of-Thought prompting can vary greatly depending on the type of task and model. For non-reasoning models, CoT generally improves average performance by a small amount, particularly if the model does not inherently engage in step-by-step processing by default. However, CoT can introduce more variability in answers, sometimes triggering occasional errors in questions the model would otherwise get right. We also found that many recent models perform some form of CoT reasoning even if not asked; for these models, a request to perform CoT had little impact. Performing CoT generally requires far more tokens (increasing cost and time) than direct answers.</li><li>For models designed with explicit reasoning capabilities, CoT prompting often results in only marginal, if any, gains in answer accuracy. However, it significantly increases the time and tokens needed to generate a response.</li></ul>Taken together, this suggests that a simple CoT prompt is generally still a useful tool for boosting average performance in non-reasoning models, especially older or smaller models that may not engage in a CoT reasoning by default. However, the gains must be weighed against increased response times and potential decreases in perfect accuracy due to more variability in answers. For dedicated reasoning models, the added benefits of explicit CoT prompting appear negligible and may not justify the substantial increase in processing time.</blockquote>

> This is the second in a series of short reports that seek to help business, education, and policy leaders understand the technical details of working with AI through rigorous testing. In this report, we investigate Chain-of-Thought (CoT) prompting, a technique that encourages a large language model (LLM) to "think step by step" (Wei et al., 2022). CoT is a widely adopted method for improving reasoning tasks, however, our findings reveal a more nuanced picture of its effectiveness. We demonstrate two things: > > * The effectiveness of Chain-of-Thought prompting can vary greatly depending on the type of task and model. For non-reasoning models, CoT generally improves average performance by a small amount, particularly if the model does not inherently engage in step-by-step processing by default. However, CoT can introduce more variability in answers, sometimes triggering occasional errors in questions the model would otherwise get right. We also found that many recent models perform some form of CoT reasoning even if not asked; for these models, a request to perform CoT had little impact. Performing CoT generally requires far more tokens (increasing cost and time) than direct answers. > * For models designed with explicit reasoning capabilities, CoT prompting often results in only marginal, if any, gains in answer accuracy. However, it significantly increases the time and tokens needed to generate a response. > > Taken together, this suggests that a simple CoT prompt is generally still a useful tool for boosting average performance in non-reasoning models, especially older or smaller models that may not engage in a CoT reasoning by default. However, the gains must be weighed against increased response times and potential decreases in perfect accuracy due to more variability in answers. For dedicated reasoning models, the added benefits of explicit CoT prompting appear negligible and may not justify the substantial increase in processing time.

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3. Why impartial altruists should suspend judgment under unawareness

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Researcher at the Center on Long-Term Risk. All opinions my own.

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To recap, <a href="https://forum.effectivealtruism.org/posts/a3hnfA9EnYm9bssTZ/1-the-challenge-of-unawareness-for-impartial-altruist-action-1">first</a>, we face an epistemic challenge beyond uncertainty over possible futures. Due to unawareness, we can’t conceive of many relevant futures in the first place, which makes the standard EV framework ill-suited for impartial altruistic decision-making. And <a href="https://forum.effectivealtruism.org/posts/qZS8cgvY5YrjQ3JiR/2-why-intuitive-comparisons-of-large-scale-impact-are">second</a>, we can’t trust that our intuitive comparisons of strategies’ overall consequences price in factors we’re unaware of with enough precision. We’ll need to evaluate these consequences with a framework that explicitly accounts for unawareness, that is, <a href="https://forum.effectivealtruism.org/posts/qZS8cgvY5YrjQ3JiR/2-why-intuitive-comparisons-of-large-scale-impact-are#Degrees_of_imprecision_from_unawareness">unawareness-inclusive expected value (UEV)</a>.Here, I’ll more specifically unpack the UEV model, and argue that we can’t compare the UEV of any given strategy with another. If so, we don’t have a reason to choose one strategy over another based purely on the impartial good. We should, instead, suspend judgment as far as impartial altruism is concerned. I’ll conclude by illustrating this problem with a worked example, building on our case study from <a href="https://forum.effectivealtruism.org/posts/a3hnfA9EnYm9bssTZ/1-the-challenge-of-unawareness-for-impartial-altruist-action-1#Case_study__Severe_unawareness_in_AI_safety">before</a>. (My arguments here aren’t meant to refute the specific approaches EAs have proposed for comparing strategies under unawareness. That’s for the <a href="https://forum.effectivealtruism.org/posts/pjc7w2r3Je7jgipYY/4-why-existing-approaches-to-cause-prioritization-are-not-1#Why_each_of_the_standard_approaches_is_inadequate">final post</a>.)<h1 data-internal-id="Unawareness_inclusive_expected_value__UEV__1_">Unawareness-inclusive expected value (UEV)<a href="#fn45ur167t2kg">[1]</a></h1><details class="detailsBlock"><summary class="detailsBlockTitle">Key takeaway</summary><div class="detailsBlockContent">To get the imprecise “EV” of a strategy under unawareness, we take the EV with respect to all plausible ways of precisely evaluating coarse outcomes.</div></details>Where exactly does the UEV for a given strategy come from? As we might <a href="https://forum.effectivealtruism.org/posts/a3hnfA9EnYm9bssTZ/1-the-challenge-of-unawareness-for-impartial-altruist-action-1#Introduction_to_unawareness">remember</a>, the kinds of possibilities we conceive of are coarse-grained descriptions of many possible worlds, called hypotheses. So let’s construct a rough model that reflects the imprecision of our understanding of these hypotheses, taking inspiration from <a href="https://plato.stanford.edu/entries/imprecise-probabilities/">imprecise probabilities</a> (i.e., representing beliefs with a set of probability distributions).This will get a bit technical, but here’s the TL;DR:<ul><li>Instead of pinning down a unique list of values for every hypothesis, we consider multiple ways of assigning precise values consistent with our evidence and principles. (E.g., to evaluate the hypothesis “misaligned ASI takes over”, we could entertain a range of more specific — though still coarse-grained — misalignment scenarios.)<ul><li>Aren’t these precise values, too, arbitrary? Indeed. This model is meant to be merely the least bad formalization of our vague epistemic state (as discussed in the <a href="https://forum.effectivealtruism.org/posts/qZS8cgvY5YrjQ3JiR/2-why-intuitive-comparisons-of-large-scale-impact-are#The__better_than_chance__argument__and_other_objections_to_imprecision">imprecision FAQ</a>).</li></ul></li><li>To compute a strategy’s UEV, we compute the EV wit</li></ul>... <style>.mjx-chtml {display: inline-block; line-height: 0; text-indent: 0; text-align: left; text-transform: none; font-style: normal; font-weight: normal; font-size: 100%; font-size-adjust: none; letter-spacing: normal; word-wrap: normal; word-spacing: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0; min-height: 0; border: 0; margin: 0; padding: 1px 0} .MJXc-display {display: block; text-align: center; margin: 1em 0; padding: 0} .mjx-chtml[tabindex]:focus, body :focus .mjx-chtml[tabindex] {display: inline-table} .mjx-full-width {text-align: center; 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To recap, first, we face an epistemic challenge beyond uncertainty over possible futures. Due to unawareness, we can’t conceive of many relevant futures in the first place, which makes the standard EV framework ill-suited for impartial altruistic decision-making. And second, we can’t trust that our intuitive comparisons of strategies’ overall consequences price in factors we’re unaware of with enough precision. We’ll need to evaluate these consequences with a framework that explicitly accounts for unawareness, that is, unawareness-inclusive expected value (UEV). Here, I’ll more specifically unpack the UEV model, and argue that we can’t compare the UEV of any given strategy with another. If so, we don’t have a reason to choose one strategy over another based purely on the impartial good. We should, instead, suspend judgment as far as impartial altruism is concerned. I’ll conclude by illustrating this problem with a worked example, building on our case study from before. (My arguments here aren’t meant to refute the specific approaches EAs have proposed for comparing strategies under unawareness. That’s for the final post.) Unawareness-inclusive expected value (UEV)[1] Key takeaway To get the imprecise “EV” of a strategy under unawareness, we take the EV with respect to all plausible ways of precisely evaluating coarse outcomes. Where exactly does the UEV for a given strategy come from? As we might remember, the kinds of possibilities we conceive of are coarse-grained descriptions of many possible worlds, called hypotheses. So let’s construct a rough model that reflects the imprecision of our understanding of these hypotheses, taking inspiration from imprecise probabilities (i.e., representing beliefs with a set of probability distributions). This will get a bit technical, but here’s the TL;DR: * Instead of pinning down a unique list of values for every hypothesis, we consider multiple ways of assigning precise values consistent with our evidence and principle

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I am broadly interested in theoretical computer science and neuroscience. Recently I've been thinking more about gradual disempowerment risks due to AI and potential mitigation strategies.<h2>Projects that I'm working on  </h2><ul><li>Improving the discourse on the trajectory of AGI and its potential implications - <a href="https://docs.google.com/document/u/0/d/12luHKOsgO1I-YcoBfUqUgg5STLb0daJuuWB-bzQ-2ak/edit">Superposition</a>  </li><li>Some <a href="https://docs.google.com/document/u/0/d/1HBppladcaNpgLzoH8lur3fBPEcGFAxUZtnbpamgC6U8/edit">proposals</a> on improving empowerment and accelerating AI policy making and governance  </li></ul>

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Note: This post is an invite for the retreat, as well as an expression of interest for similar events which will be conducted. We are using the form for both. TL;DR: Ooty AI Retreat 2.0 (June 23-30, '25, open to all): We're moving beyond e/acc vs doomer debates to practically test AI tools & opportunities. We'll cultivate post-rational flexibility (multiple perspectives, intuition, meditation) through coding, writing sprints, strategy talks, and more. Interested? <a href="https://docs.google.com/forms/d/e/1FAIpQLSey8PO_9Rw96uydgOnRDLIYuEpaVzYbH_nDYlCI7FeOkMbOjA/viewform?usp=header">Fill the form! </a>(deadline : June 17th) Hey folks,We're running our Ooty AI Alignment Retreat 2.0  from June 23-30, 2025. The last one happened in June 2024. This is open to non technical people also! When we say x risks, we don't only mean just threat models and p doom/takeoff discussions. We want to work on identifying and navigating the opportunities that current AI capabilities unlock, striving for nuanced understanding over simplistic e/acc versus doomer dichotomies. We welcome people from all kinds of backgrounds, worldviews, and hope to have a productive discussion. Crucially, we anchor these explorations with quick empirical tests and tight feedback loops with reality and today's models—engaging in a '<a href="https://www.overcomingbias.com/p/near-far-summaryhtml">near mode</a>' rather than getting lost in purely abstract futures.When we say post rationality, we are cultivating the ability to hold multiple, sometimes even contradictory, mental models or "systems" at once, and fluidly switching between them depending on the situation. This often involves re-engaging with things previously dismissed as vestigial like intuition, emotions, embodiment, or even spiritual concepts—not as blind belief, but as potentially useful sources of information or ways to operate. As the memetic environment becomes increasingly adversarial, having tools like metacognitive skills and contemplative technique are important to ensure coordination (alignment) with ourselves across time, integrating the different mind parts, giving us stability to handle these turbulent times better. To get a sense of what happened last year,<h2> Last year's events</h2>Some of the sessions that we had were,<ul><li>The future of Brain Machine Interfaces, discussion on bandwidth constraints, etc</li><li>Talks on Centaurism, Cyborgism and integration between AI and humans</li><li>Sahil gave an introduction to his agenda of <a href="https://www.alignmentforum.org/s/aMz2JMvgXrLBkq4h3">Live Theory</a></li><li>Concrete predictions on the future of AI via manifold, forecasting </li></ul>...

Note: This post is an invite for the retreat, as well as an expression of interest for similar events which will be conducted. We are using the form for both. TL;DR: Ooty AI Retreat 2.0 (June 23-30, '25, open to all): We're moving beyond e/acc vs doomer debates to practically test AI tools & opportunities. We'll cultivate post-rational flexibility (multiple perspectives, intuition, meditation) through coding, writing sprints, strategy talks, and more. Interested? Fill the form! (deadline : June 17th) Hey folks, We're running our Ooty AI Alignment Retreat 2.0 from June 23-30, 2025. The last one happened in June 2024. This is open to non technical people also! When we say x risks, we don't only mean just threat models and p doom/takeoff discussions. We want to work on identifying and navigating the opportunities that current AI capabilities unlock, striving for nuanced understanding over simplistic e/acc versus doomer dichotomies. We welcome people from all kinds of backgrounds, worldviews, and hope to have a productive discussion. Crucially, we anchor these explorations with quick empirical tests and tight feedback loops with reality and today's models—engaging in a 'near mode' rather than getting lost in purely abstract futures. When we say post rationality, we are cultivating the ability to hold multiple, sometimes even contradictory, mental models or "systems" at once, and fluidly switching between them depending on the situation. This often involves re-engaging with things previously dismissed as vestigial like intuition, emotions, embodiment, or even spiritual concepts—not as blind belief, but as potentially useful sources of information or ways to operate. As the memetic environment becomes increasingly adversarial, having tools like metacognitive skills and contemplative technique are important to ensure coordination (alignment) with ourselves across time, integrating the different mind parts, giving us stability to handle these turbulent times be

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A fun index of the concepts from The Sequences in the form of several litanies. These litanies are neither a guide nor an introduction to The Way of Rationality because rationality is an <a href="https://www.lesswrong.com/posts/teaxCFgtmCQ3E9fy8/the-martial-art-of-rationality">art</a> not a series of precipes. Rather, these litanies serve as a tool; for meditating on, for connecting ideas; or for preparing for <a href="https://www.lesswrong.com/s/5bZZZJ5psXrrD5BGb/p/kXAb5riiaJNrfR8v8">The Ritual Of Changing One’s Mind</a>. For some this may be a useful exercise, for others not. If they help you, Enjoy!<h1 data-internal-id="h.k4oy3jrmqzf3">Litanies Of The Way</h1>Litany On LitaniesLitany Against BiasLitany For CommunicationLitany Against PoliticsLitany For InquiryLitany For Letting GoLitany For BeliefLitany Against The DarkLitany For SeeingLitany For CuriosityLitany For Minds<h1 data-internal-id="Litany_On_Litanies">Litany On Litanies</h1>I will use these litanies to grow <a href="https://www.lesswrong.com/posts/DoLQN5ryZ9XkZjq5h/tsuyoku-naritai-i-want-to-become-stronger">stronger</a>And when they are no longer useful I will not <a href="https://www.lesswrong.com/tag/litany-of-gendlin">mourn</a>For although an apprentice must first imitate The Way that is knownA master must discover The Way that is to come<h1 data-internal-id="Litany_Against_Bias">Litany Against Bias</h1>When I learn about a bias I will not use it as a <a href="https://www.lesswrong.com/tag/fully-general-counterargument">Fully General Counterargument</a>And when I teach others of biasI will <a href="https://www.lesswrong.com/s/GSqFqc646rsRd2oyz/p/AdYdLP2sRqPMoe8fb">first do no harm</a>When new evidence comes inI will <a href="https://www.lesswrong.com/posts/Yq6aA4M3JKWaQepPJ/burdensome-details">feel the burden of every detail</a>When I’m considering my plansI will <a href="https://www.lesswrong.com/posts/CPm5LTwHrvBJCa9h5/planning-fallacy">view them from the outside</a>  When I want to be an effective altruistI will not <a href="https://www.lesswrong.com/lw/hw/scope_insensitivity/">purchase satisfaction</a>When the world is chaotic I will <a href="https://www.lesswrong.com/posts/msJA6B9ZjiiZxT6EZ/lawful-uncertainty">act lawfully</a> When there exist justifications beyond those givenI will <a href="https://www.lesswrong.com/s/pmHZDpak4NeRLLLCw/p/KZLa74SzyKhSJ3M55">reject the genetic heuristic</a>When I am presented with a false dilemmaI will <a href="https://www.lesswrong.com/posts/erGipespbbzdG5zYb/the-third-alternative">look for a Third Alternative</a>And when I wish to overcome bias I will <a href="https://www.lesswrong.com/s/3ELrPerFTSo75WnrH/p/i8q4vXestDkGTFwsc">know it is a worthy goal</a><h1 data-internal-id="Litany_For_Communication">Litany For Communication</h1>When I feel I am misunderstood I will <a href="https://www.lesswrong.com/s/zpCiuR4T343j9WkcK/p/sSqoEw9eRP2kPKLCz">be aware that my intent might not be transparent</a>When I need to communicate something complexI will <a href="https://www.lesswrong.com/s/zpCiuR4T343j9WkcK/p/HLqWn5LASfhhArZ7w">lay out an inferential pathway</a>  When making propositions I will not use <a href="https://www.lesswrong.com/s/GSqFqc646rsRd2oyz/p/bfbiyTogEKWEGP96S">Fake Justifications</a>When I am attacking propositions  I will give my <a href="https://www.lesswrong.com/s/GSqFqc646rsRd2oyz/p/TGux5Fhcd7GmTfNGC">True Rejection</a><h1 data-internal-id="Litany_Against_Politics">Litany Against Politics</h1><a href="https://www.lesswrong.com/posts/9weLK2AJ9JEt2Tt8f/politics-is-the-mind-killer">Politics is the Mind-Killer</a>When observing a debate I will <a href="https://www.lesswrong.com/posts/PeSzc9JTBxhaYRp9b/policy-debates-should-not-appear-one-sided">not treat arguments like soldiers</a>When I am dealing with a non-binary question I will <a href="https://www.lesswrong.com/posts/XYCEB9roxEBfgjfxs/the-scales-of-justice-the-notebook-of-rationality">relinquish my scales and take up a notebook</a>When the problem is not just black and whiteI will <a href="https://www.lesswrong.com/s/FrqfoG3LJeCZs96Ym/p/dLJv2CoRCgeC2mPgj">distinguish shades of grey</a> When I see someone kicking a vending machineI will <a href="https://www.lesswrong.com/tag/correspondence-bias">not hypothesize that they are a mutant</a><h1 data-internal-id="Litany_For_Inquiry">Litany For Inquiry</h1>When I ponder I will <a href="https://www.lesswrong.com/s/3ELrPerFTSo75WnrH/p/Lz64L3yJEtYGkzMzu">let the meaning choose my words</a>When I’m forming a hypothesisI will <a href="https://www.lesswrong.com/posts/rmAbiEKQDpDnZzcRf/positive-bias-look-into-the-dark">look into the dark</a> When I question I will <a href="https://www.lesswrong.com/s/3ELrPerFTSo75WnrH/p/2jp98zdLo898qExrr">Hug the Query</a>When I attack my b...

A fun index of the concepts from The Sequences in the form of several litanies. These litanies are neither a guide nor an introduction to The Way of Rationality because rationality is an art not a series of precipes. Rather, these litanies serve as a tool; for meditating on, for connecting ideas; or for preparing for The Ritual Of Changing One’s Mind. For some this may be a useful exercise, for others not. If they help you, Enjoy! Litanies Of The Way Litany On Litanies Litany Against Bias Litany For Communication Litany Against Politics Litany For Inquiry Litany For Letting Go Litany For Belief Litany Against The Dark Litany For Seeing Litany For Curiosity Litany For Minds Litany On Litanies I will use these litanies to grow stronger And when they are no longer useful I will not mourn For although an apprentice must first imitate The Way that is known A master must discover The Way that is to come Litany Against Bias When I learn about a bias I will not use it as a Fully General Counterargument And when I teach others of bias I will first do no harm When new evidence comes in I will feel the burden of every detail When I’m considering my plans I will view them from the outside When I want to be an effective altruist I will not purchase satisfaction When the world is chaotic I will act lawfully When there exist justifications beyond those given I will reject the genetic heuristic When I am presented with a false dilemma I will look for a Third Alternative And when I wish to overcome bias I will know it is a worthy goal Litany For Communication When I feel I am misunderstood I will be aware that my intent might not be transparent When I need to communicate something complex I will lay out an inferential pathway When making propositions I will not use Fake Justifications When I am attacking propositions I will give my True Rejection Litany Against Politics Politics is the Mind-Killer When observing a debate I will not tre

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A few jobs ago, I worked at company that collected data from disparate sources, then processed and deduplicated it into spreadsheets for ingestion by the data science and customer support teams. Some common questions the engineering team got were:<ul><li>Why is the data in some input CSV missing in the output?</li><li>Why is data in the output CSV not matching what we expect?</li></ul>To debug these problems, the process was to try to reverse engineer where the data came from, then try to guess which path that data took through the monolithic data processor.This is the story of how we stopped doing that, and started storing references to all source data for every piece of output data.(This is reconstructed from memory so I no longer claim 100% accuracy)<h2>Get Source Data Into Your Database</h2>The good news is that before I even started at this company, it was well understood that we needed to keep our data somewhere durable, since we might need to reprocess it one day. This involved putting all of the data in an S3 bucket, organized by source and date.In order to tie our outputs to our inputs, we needed to first get our inputs into the database. We previously had some intermediate formats, but we wanted to know exactly where our data was coming from. So we added a source table:<h3>csv_source</h3><figure class="table"><table><thead><tr><th>id</th><th>filename</th></tr></thead><tbody><tr><td>1</td><td>client-x-2024-07-01.csv</td></tr><tr><td>2</td><td>client-x-2024-08-01.csv</td></tr></tbody></table></figure><h3>csv_data</h3><figure class="table"><table><thead><tr><th>id</th><th>csv_source_id</th><th>first_name</th><th>last_name</th><th>job</th></tr></thead><tbody><tr><td>10</td><td>1</td><td>Brendan</td><td>Long</td><td>Basket Weaver</td></tr><tr><td>11</td><td>2</td><td>Brendan</td><td>Long</td><td>Senior Basket Weaver</td></tr><tr><td>12</td><td>2</td><td>Example</td><td>McExampleton</td><td>Basket Weaver</td></tr></tbody></table></figure>(Ok, so we didn't literally upload the CSV and only uploaded it after determining the standardized column names, <a href="https://github.com/brendanlong/any_columns">sort-of like this</a>)<h2>Tie Your Output Data to the Source Data</h2>Now that we had our source data, so the next step was to tie our output data to it, so we could start to answer questions like "Where did this Brendan Long guy come from?" and "Where did this Basket Weaver job come from?". Our output data frequently needed to deduplicate data from multiple sources, and the obvious choice here would be to link to the source of the winning data, but for debugging, we don't just want to know about the winning data, we want to know about all of it.So we added all all of the sources for a piece of data. We did this with join tables, but the examples will show them as inline arrays to keep this readable.<h3>jobs</h3><figure class="table"><table><thead><tr><th>id</th><th>csv_data_ids</th><th>name</th></tr></thead><tbody><tr><td>20</td><td>{10,12}</td><td>Basket Weaver</td></tr><tr><td>21</td><td>{11}</td><td>Senior Basket Weaver</td></tr></tbody></table></figure><h3>employees</h3><figure class="table"><table><thead><tr><th>id</th><th>csv_data_ids</th><th>first_name</th><th>last_name</th><th>job_id</th></tr></thead></table></figure>...

A few jobs ago, I worked at company that collected data from disparate sources, then processed and deduplicated it into spreadsheets for ingestion by the data science and customer support teams. Some common questions the engineering team got were: * Why is the data in some input CSV missing in the output? * Why is data in the output CSV not matching what we expect? To debug these problems, the process was to try to reverse engineer where the data came from, then try to guess which path that data took through the monolithic data processor. This is the story of how we stopped doing that, and started storing references to all source data for every piece of output data. (This is reconstructed from memory so I no longer claim 100% accuracy) Get Source Data Into Your Database The good news is that before I even started at this company, it was well understood that we needed to keep our data somewhere durable, since we might need to reprocess it one day. This involved putting all of the data in an S3 bucket, organized by source and date. In order to tie our outputs to our inputs, we needed to first get our inputs into the database. We previously had some intermediate formats, but we wanted to know exactly where our data was coming from. So we added a source table: csv_source idfilename1client-x-2024-07-01.csv2client-x-2024-08-01.csv csv_data idcsv_source_idfirst_namelast_namejob101BrendanLongBasket Weaver112BrendanLongSenior Basket Weaver122ExampleMcExampletonBasket Weaver (Ok, so we didn't literally upload the CSV and only uploaded it after determining the standardized column names, sort-of like this) Tie Your Output Data to the Source Data Now that we had our source data, so the next step was to tie our output data to it, so we could start to answer questions like "Where did this Brendan Long guy come from?" and "Where did this Basket Weaver job come from?". Our output data frequently needed to deduplicate data from multiple sources, and the obvious choi

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When our kids were 7 and 5 they started walking home from school alone. We wrote explaining they were ready and giving permission, the school had a few reasonable questions, and that was it. Just kids walking home from the local public school like they have in this neighborhood for generations. Online, however, it's common for people to write as if this sort of thing is long gone. Zvi <a href="https://thezvi.substack.com/p/letting-kids-be-kids">captures a common view</a>: <blockquote> You want to tell your kids, go out and play, be home by dinner, like your father and his father before him. But if you do, or even if you tell your kids to walk the two blocks to school, eventually a policeman will show up at your house and warn you not to do it again, or worse. And yes, you'll be the right legally, but what are you going to do, risk a long and expensive legal fight? So here we are, and either you supervise your kids all the time or say hello to a lot of screens. </blockquote> His post also references ~eight news stories where a family had trouble with authorities because they let their kid do things that should be ordinary, like walking to a store at age nine. It's not just Zvi: parents who would like kids to have more freedom often focus on the risk, with the potential for police or Child Protective Services to get involved. While it's important to understand and mitigate the risks, amplifying the rare stories that go poorly magnifies their chilling effect and undermines the overall effort. I showed the quote to our oldest, now 11 and comfortable on her own: "I sincerely doubt that a police officer would get mad at me for walking to school or to the corner store by myself." She got to this level of comfort by spending a lot of time out in our <a href="https://en.wikipedia.org/wiki/Somerville,_Massachusetts">walkable kid-friendly neighborhood</a>. Sometimes with us, and increasingly on her own. For example it's raining today and she just came back to the house to tell me that she was grabbing rain gear and then she was going puddle jumping with two younger neighborhood kids. In a bit I'll stop writing and take her younger sister (age 3) out to join in. <a href="https://www.jefftk.com/ghiblified-version-of-kids-playing-in-puddle-big.png"><img src="https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/KvB28n3CPwourTFFW/n7jw0mfdlseypp2aqr1t" srcset="https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/KvB28n3CPwourTFFW/n7jw0mfdlseypp2aqr1t 550w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/KvB28n3CPwourTFFW/mcdvrvghttkigejg8myf 1100w"></a><div></div> Some other examples of being out alone: <ul> <li>Heading to a school concert the 8yo was running late and the 10yo was getting impatient. I asked her: "you know the way, do you want to go on ahead by yourself?" She walked the half mile without issue, with her <a href="https://www.jefftk.com/p/gizmo-watch-review">watch</a> as backup. </li> <li>Both older kids will go to the corner store to spend </li></ul>...

When our kids were 7 and 5 they started walking home from school alone. We wrote explaining they were ready and giving permission, the school had a few reasonable questions, and that was it. Just kids walking home from the local public school like they have in this neighborhood for generations. Online, however, it's common for people to write as if this sort of thing is long gone. Zvi captures a common view: > You want to tell your kids, go out and play, be home by dinner, like your father and his father before him. But if you do, or even if you tell your kids to walk the two blocks to school, eventually a policeman will show up at your house and warn you not to do it again, or worse. And yes, you'll be the right legally, but what are you going to do, risk a long and expensive legal fight? So here we are, and either you supervise your kids all the time or say hello to a lot of screens. His post also references ~eight news stories where a family had trouble with authorities because they let their kid do things that should be ordinary, like walking to a store at age nine. It's not just Zvi: parents who would like kids to have more freedom often focus on the risk, with the potential for police or Child Protective Services to get involved. While it's important to understand and mitigate the risks, amplifying the rare stories that go poorly magnifies their chilling effect and undermines the overall effort. I showed the quote to our oldest, now 11 and comfortable on her own: "I sincerely doubt that a police officer would get mad at me for walking to school or to the corner store by myself." She got to this level of comfort by spending a lot of time out in our walkable kid-friendly neighborhood. Sometimes with us, and increasingly on her own. For example it's raining today and she just came back to the house to tell me that she was grabbing rain gear and then she was going puddle jumping with two younger neighborhood kids. In a bit I'll stop writing and take her yo

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Back in the mists of time, there was this game called World of Warcraft, where you and your four closest friends would team up to complete dungeons together. To handle the inevitable people with no friends, they added <a href="https://wowpedia.fandom.com/wiki/Meeting_Stone">meeting stones</a> near each dungeon. When you clicked a meeting stone, it added you to a queue, and once five people were in the queue, you would all be added to a group together.I was thinking abut this at LessOnline last week, since I <s>don't have any friends</s> sometimes had trouble finding groups of people to talk to. One way to handle this was to walk up to an existing group and talk to them, but this was tricky since sometimes all of the existing groups were 4+ people already. My strategy ended up being to sit on an empty couch in the busiest areas and wait for people to come to me, but sometimes there were no empty couches in the busy areas.This made me wish we had some (virtual) meeting stones:<ol><li>On WriteHaven, you click "Looking For Group".</li><li>Three<a href="#fne5bx7fan6zc">[1]</a> additional people click "Looking For Group".</li><li>WriteHaven notifies me that a group has been formed and gives me a randomly-selected location<a href="#fnd5bcag5x6en">[2]</a> that I've been summoned to.</li></ol>This could also potentially be a non-WriteHaven app, and I'm tempted to just write it myself, but it would only really be useful if everyone at a conference was aware of it.What do you all think?<ol class="footnote-section footnotes" data-footnote-section="" role="doc-endnotes"><li class="footnote-item" data-footnote-item="" data-footnote-index="1" data-footnote-id="e5bx7fan6zc" role="doc-endnote" id="fne5bx7fan6zc"><a href="#fnrefe5bx7fan6zc">^</a><div class="footnote-content" data-footnote-content="">The ideal conversation size might be smaller than four, but groups of two can lead to being matched with one other person who you don't actually end up liking, and not having an non-awkward way to extract yourself.</div></li><li class="footnote-item" data-footnote-item="" data-footnote-index="2" data-footnote-id="d5bcag5x6en" role="doc-endnote" id="fnd5bcag5x6en"><a href="#fnrefd5bcag5x6en">^</a><div class="footnote-content" data-footnote-content="">The conference organizers can setup a list of locations to randomly select from, like "The fire table near the Diagonal Hall".</div></li></ol>

Back in the mists of time, there was this game called World of Warcraft, where you and your four closest friends would team up to complete dungeons together. To handle the inevitable people with no friends, they added meeting stones near each dungeon. When you clicked a meeting stone, it added you to a queue, and once five people were in the queue, you would all be added to a group together. I was thinking abut this at LessOnline last week, since I don't have any friends sometimes had trouble finding groups of people to talk to. One way to handle this was to walk up to an existing group and talk to them, but this was tricky since sometimes all of the existing groups were 4+ people already. My strategy ended up being to sit on an empty couch in the busiest areas and wait for people to come to me, but sometimes there were no empty couches in the busy areas. This made me wish we had some (virtual) meeting stones: 1. On WriteHaven, you click "Looking For Group". 2. Three[1] additional people click "Looking For Group". 3. WriteHaven notifies me that a group has been formed and gives me a randomly-selected location[2] that I've been summoned to. This could also potentially be a non-WriteHaven app, and I'm tempted to just write it myself, but it would only really be useful if everyone at a conference was aware of it. What do you all think? 1. ^ The ideal conversation size might be smaller than four, but groups of two can lead to being matched with one other person who you don't actually end up liking, and not having an non-awkward way to extract yourself. 2. ^ The conference organizers can setup a list of locations to randomly select from, like "The fire table near the Diagonal Hall".

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MRI scans and <a href="https://en.wikipedia.org/wiki/Positron_emission_tomography">PET scans</a> are different methods for medical imaging, and they observe different things: <ul> <li> MRI scans can see which nuclei are present in regions. Sometimes bond types of particular elements can be distinguished, eg in <a href="https://en.wikipedia.org/wiki/Proton_nuclear_magnetic_resonance">1H NMR</a> and <a href="https://en.wikipedia.org/wiki/Functional_magnetic_resonance_imaging">fMRI</a>. </li> <li> PET scans add a short-lived radioactive tracer (usually organic molecules with radioactive fluorine added to them) and observe where decays happen. This can track things like movement of glucose in the body. The half-life of fluorine-18 is ~110 minutes. PET scans are often combined with CT or MRI to correlate tracers with locations of organs. </li> </ul> Of course, MRI scans are generally preferably because they don't expose patients or staff to radiation, and don't require short-lived radioactive compounds. That being the case, who among us hasn't asked: <blockquote> Could we use tracer compounds for MRI scans that let MRI do what PET is used for? </blockquote> Historically, such tracers for MRI weren't available, but there are now some interesting options. (Yes, metallic contrast agents (eg gadolinium) have been used with MRI, but that doesn't do what we need here.) <h2>hyperpolarized carbon-13</h2> 1.1% of carbon is 13C, which is stable and basically harmless. Hyperpolarized 13C has a fairly strong MRI signal and <a href="https://pmc.ncbi.nlm.nih.gov/articles/PMC10546364/">can be used</a> as a MRI tracer. You can make tracers using that by: <ol> <li>Getting some 13C.</li> <li>Making some relevant molecule, like pyruvate or acetate. Unlike PET tracers, this doesn't need to be done right before they're used.</li> <li>Polarizing the 13C. Basically, you get it very cold, and put it in a strong magnet until it goes all wibbly.</li> </ol> While the compounds are stable, the polarization decays quickly. Pyruvate spin lattice relaxation time (T1) is ~50-70 seconds ex vivo and ~20-30 seconds in vivo. Sufficient signal for detection is present for ~5x the T1. In that time, the tracer needs to be warmed up, injected, and detected by a scan.So, you need a strong magnet, and special equipment for quickly processing the tracer during a MRI scan. Few hospitals are currently set up to do this, but technologically it's easier than the MRI scan itself. <h2>nitroxide radicals</h2> An obvious idea for MRI tracers is to use a metal contrast agent (eg gadolinium) in some organic complex attached to an antibody. That didn't work very well. <a href="https://journals.lww.com/investigativeradiology/abstract/1985/10000/magnetic_resonance_imaging_using_gadolinium.8.aspx">This 1985 paper</a> notes: <blockquote> For monoclonal antibodies to function as selective MR contrast agents, substantial advances in technology must occur. </blockquote> Peo...

MRI scans and PET scans are different methods for medical imaging, and they observe different things: * MRI scans can see which nuclei are present in regions. Sometimes bond types of particular elements can be distinguished, eg in 1H NMR and fMRI. * PET scans add a short-lived radioactive tracer (usually organic molecules with radioactive fluorine added to them) and observe where decays happen. This can track things like movement of glucose in the body. The half-life of fluorine-18 is ~110 minutes. PET scans are often combined with CT or MRI to correlate tracers with locations of organs. Of course, MRI scans are generally preferably because they don't expose patients or staff to radiation, and don't require short-lived radioactive compounds. That being the case, who among us hasn't asked: > Could we use tracer compounds for MRI scans that let MRI do what PET is used for? Historically, such tracers for MRI weren't available, but there are now some interesting options. (Yes, metallic contrast agents (eg gadolinium) have been used with MRI, but that doesn't do what we need here.) hyperpolarized carbon-13 1.1% of carbon is 13C, which is stable and basically harmless. Hyperpolarized 13C has a fairly strong MRI signal and can be used as a MRI tracer. You can make tracers using that by: 1. Getting some 13C. 2. Making some relevant molecule, like pyruvate or acetate. Unlike PET tracers, this doesn't need to be done right before they're used. 3. Polarizing the 13C. Basically, you get it very cold, and put it in a strong magnet until it goes all wibbly. While the compounds are stable, the polarization decays quickly. Pyruvate spin lattice relaxation time (T1) is ~50-70 seconds ex vivo and ~20-30 seconds in vivo. Sufficient signal for detection is present for ~5x the T1. In that time, the tracer needs to be warmed up, injected, and detected by a scan. So, you need a strong magnet, and special equipment for quickly processing the tracer during a MRI scan. Few hos

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Last year I got my friend a gift card to <a href="https://beastandcleaver.com/">Beast and Cleaver</a> for his wedding, a local butcher shop and restaurant.I was pretty proud of the gift. He and his wife said the meal was amazing and I wouldn't expect them to say that if it weren't true. Getting something on the wedding registry or perhaps a gift card would have been safer, but I don't think it would have yielded as much joy.I can't take too much credit though. As a <a href="https://www.yelp.com/user_details?userid=dnsCCmwYrONjUAivm3XR4w">Yelp Elite</a> food critic with over a decade of experience churning out high quality restaurant reviews, I like to think I have pretty good taste in food. But this time it wasn't me. It was Kenji.I was watching a <a href="https://youtu.be/iInHnbGbsNQ?si=dVvhSmogpuDWIAiI&t=12">YouTube video</a> by Kenji Lopez-Alt, a food writer who actually does have good taste in food. In his video he mentions Beast and Cleaver and says<a href="#fn6kd8v1wxp1m">[1]</a> that it's a very good butcher shop. I've read, watched and listened to a ton of Kenji's content over the years and think very highly of him.So really what's happening here is something I'll call second order taste. First order taste would be me eating at Beast and Cleaver and determining it to be good. Second order taste is me identifying Kenji as someone with good first order taste.I think that having good second order taste is pretty important. And I'm talking about taste more generally, not just as it relates to food. The world is complex. We can't evaluate everything ourselves. We don't have the time or the skills. For better or for worse, we often have to figure out who the trustworthy people are and listen to them.Over the years I've identified a handful of people in various domains who I respect and see as highly trustworthy. Paul Ingraham for pain. Tori Olds for therapy. Ben Felix for personal finance. Ben Taylor for basketball. Demand Curve for marketing. Peter Attia for health. I think all of these people are quite smart and really know their field.And then there's websites like <a href="https://thebestbikelock.com/">The Best Bike Lock</a>. I bought a moderately expensive e-bike recently. I store it in my apartment complex's bike room but historically there's been a fair amount of theft in that room, including my own electric scooter. I was dumb and only used a cable lock to secure the scooter, but there are also two U-locks that have been cut through and left sitting there in the bike room as well, so the theft is clearly going beyond low hanging fruit like cable locks.Anyway, I wanted to do some research into how I ...

Last year I got my friend a gift card to Beast and Cleaver for his wedding, a local butcher shop and restaurant. I was pretty proud of the gift. He and his wife said the meal was amazing and I wouldn't expect them to say that if it weren't true. Getting something on the wedding registry or perhaps a gift card would have been safer, but I don't think it would have yielded as much joy. I can't take too much credit though. As a Yelp Elite food critic with over a decade of experience churning out high quality restaurant reviews, I like to think I have pretty good taste in food. But this time it wasn't me. It was Kenji. I was watching a YouTube video by Kenji Lopez-Alt, a food writer who actually does have good taste in food. In his video he mentions Beast and Cleaver and says[1] that it's a very good butcher shop. I've read, watched and listened to a ton of Kenji's content over the years and think very highly of him. So really what's happening here is something I'll call second order taste. First order taste would be me eating at Beast and Cleaver and determining it to be good. Second order taste is me identifying Kenji as someone with good first order taste. I think that having good second order taste is pretty important. And I'm talking about taste more generally, not just as it relates to food. The world is complex. We can't evaluate everything ourselves. We don't have the time or the skills. For better or for worse, we often have to figure out who the trustworthy people are and listen to them. Over the years I've identified a handful of people in various domains who I respect and see as highly trustworthy. Paul Ingraham for pain. Tori Olds for therapy. Ben Felix for personal finance. Ben Taylor for basketball. Demand Curve for marketing. Peter Attia for health. I think all of these people are quite smart and really know their field. And then there's websites like The Best Bike Lock. I bought a moderately expensive e-bike recently. I store it in my apartment c

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<h1>tl;dr</h1>In quantitative finance, the value of forecasting is obvious. No decisions are made without forecasts, and every forecast is created for some decision.On <a href="https://www.metaculus.com/">Metaculus</a><a href="https://jordanmrubin.substack.com/p/dimensionalizing-forecast-value#footnote-1-163741438">1</a>, the signal is just as sharp, but the market for the signal is fuzzier. The gap isn’t quality: it’s that the value proposition is implicit.This post is my <a href="https://www.lesswrong.com/posts/LSFiKt4zGxXcX2oxi/dimensionalization">dimensionalization</a> of forecast value: an identification of the unique factors that determine the importance of answering a question. These factors are Clarity, Leverage, and Efficiency (CLE).I use CLE to decide when an answer will be valuable, before I spend time asking or answering the question.To find value, I focus on questions where:<ul><li>I might change my mind (Clarity)</li><li>A lot is at stake (Leverage)</li><li>Effort compounds over time (Efficiency)</li></ul><hr> <h1>Decisions Create Questions</h1>Forecasts are useful for making decisions under uncertainty. This is a specific context. It is not always the relevant context.Much existing content on forecasting assumes a question worth asking, and focuses on methodology improvements. Predicting the future is an unsolved, important problem; it’s good that smart people are working on these improvements.But suppose I can get a decent forecast answer to any question about the future. What questions should I actually try to answer?I don’t need a forecast if:<ul><li>I am not making a decision</li><li>I already know what choice I want to make</li><li>It doesn’t matter what I decide</li></ul>Even in a decision-making context, forecasts are also less helpful when:<ul><li>I can’t observe the outcome of my decision</li><li>I can’t trust the forecaster</li><li>I have no time to decide</li></ul>Forecasts are more helpful when:<ul><li>The event repeats</li><li>Mistakes are highly visible</li><li>Catastrophic outcomes are possible</li><li>The decision matters</li><li>I don’t know what to do</li></ul>How do I take this mess of factors and figure out what questions are worth answering?<hr><h1>Dimensionalizing Forecast Value: CLE</h1>This section is a proposed framework for prioritizing forecasts.There are many sources of uncertainty, and many decisions to make. How do I know what questions I want answered?I will focus on three sources of value from forecasts:<ol><li>Clarity: Forecasting can help me make a decision when I don’t know what to do.</li><li>Leverage: Forecasting can help me make the best decision when it matters most.</li><li>Efficiency: Forecasting now can help me make even better decisions next time.</li></ol>These are the core forecasting value drivers<a href="https://jordanmrubin.substack.com/p/dimensionalizing-forecast-value#footnote-2-163741438">2</a>. 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tl;dr In quantitative finance, the value of forecasting is obvious. No decisions are made without forecasts, and every forecast is created for some decision. On Metaculus1, the signal is just as sharp, but the market for the signal is fuzzier. The gap isn’t quality: it’s that the value proposition is implicit. This post is my dimensionalization of forecast value: an identification of the unique factors that determine the importance of answering a question. These factors are Clarity, Leverage, and Efficiency (CLE). I use CLE to decide when an answer will be valuable, before I spend time asking or answering the question. To find value, I focus on questions where: * I might change my mind (Clarity) * A lot is at stake (Leverage) * Effort compounds over time (Efficiency) ---------------------------------------- Decisions Create Questions Forecasts are useful for making decisions under uncertainty. This is a specific context. It is not always the relevant context. Much existing content on forecasting assumes a question worth asking, and focuses on methodology improvements. Predicting the future is an unsolved, important problem; it’s good that smart people are working on these improvements. But suppose I can get a decent forecast answer to any question about the future. What questions should I actually try to answer? I don’t need a forecast if: * I am not making a decision * I already know what choice I want to make * It doesn’t matter what I decide Even in a decision-making context, forecasts are also less helpful when: * I can’t observe the outcome of my decision * I can’t trust the forecaster * I have no time to decide Forecasts are more helpful when: * The event repeats * Mistakes are highly visible * Catastrophic outcomes are possible * The decision matters * I don’t know what to do How do I take this mess of factors and figure out what questions are worth answering? ---------------------------------------- Dimensionalizing Forec

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A year ago, I decided to reduce my employment level from 100% to 80% and to take Fridays off.My main motivation was to have some time for myself: Relax, reduce my stress level from work, have more time for side projects, do more sports, and maybe spend more time with my wife without the kids. This was only partially successful as there were a few things I had not realized beforehand.I’d like to describe my experience, and will focus on the effects this change had on my work, financial situation, side projects, and personal life / health.To give some background, I’ll start with my personal situation: I have two small kids and spend the mornings, evenings and the weekends with them. During the day I’m working. This schedule does not leave much time for myself. When the kids go to bed in the evenings, I’m usually too tired to do anything meaningful.While I worked 80%, I put the kids to childcare during my day off.Work The four days I spend at work have become very fast-paced and concentrated. There is less time for chats and social interactions at work, and I’m more focused on getting stuff done.Some activities do not reduce by 20% but have a fixed time requirement per week or month (e.g., weekly team meetings). These will now fall in 4 instead of 5 days, leaving over-proportionally less time to get work done.Additionally, this increased density of meetings during the 4 days means there will be fewer long time blocks available to get deep work done.Your colleagues need to adjust, e.g., they need to know that you don’t work on Fridays. While you can make this clear by putting a blocker in your calendar, they also need to consider your reduced availability in their planning.There is also a risk that management and your colleagues only slowly adjust their expectations to your reduced level of output.As a result, I believe it is proportionally harder to be productive at the new employment level, while at the same time your colleagues and management may only slowly adjust.Financial situation The obvious first-order impact of reducing your employment level by 20% is that you will earn 20% less salary.However, there are a few less obvious things to consider in this calculation:The income tax progression means the top 20% of your income are taxed higher than the remaining 80%, meaning that after deduction of income tax, a reduction of 20% gross salary will translate in...

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I’ve been working on a series of longer articles explaining and justifying the following principles of communicating AI risk to the general public, but in the interest of speed and brevity, I’ve decided to write a simplified guide here. Some of this will be based on articles I’ve already written and released, and some will be based on articles that are still in the works.                   Principle 1- Jargon                  It’s best to avoid jargon when communicating with the general public. Jargon is not easily accessible for non-experts, and unfamiliar words and phrases could lead to misunderstandings. However, when communicating about new technologies that are largely unprecedented, some jargon may be necessary. When you must use jargon, make sure you immediately follow the term with a simple and concise definition. Don’t stack multiple terms together into the same line, and don’t stack more jargon into your definition.                  Be consistent with your jargon. Use the same terms consistently, until the public gets used to them. If you notice the public already seems familiar with a term, or experts are using the term often in public communication, continue to use the same term. When you are able, use catchy, memetic terms that carry some emotional weight. An example of this type of term is “the black-box problem” when discussing interpretability, because not only is “black box problem” rather catchy and descriptive, but a “black box” is already associated with frightening events such as plane crashes.  Principle 2- Sound Bites                  Anticipate that anything you produce- whether written, audio, or audio/visual- will be broken up into short-form content such as tweets or tik-tok videos. Because you can anticipate that your content will be shredded into sound bites, it’s important that you ensure each argument can stand on its own. Nuance is still important, but try to incorporate caveats into a short space following the nuance you’ve introduced.                   If you cannot make each argument stand on its own in just a f...

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Software engineer from Ukraine, currently living and working in Estonia. I mainly specialize in computer vison & robotics. https://grgv.xyz/.

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(This is cross-posted from my blog at <a href="https://grgv.xyz/blog/neurons1/">https://grgv.xyz/blog/neurons1/</a>. I'm looking for feedback: does it makes sense at all, and if there is any novelty. Also, if the folloup questions/directions make sense)While applying <a href="https://dynalist.io/d/n2ZWtnoYHrU1s4vnFSAQ519J#z=disz2gTx-jooAcR0a5r8e7LZ">logit attribution analysis</a> to transformer outputs, I have noticed that in many cases the generated token can be attributed to the output of a single neuron.One way to analyze neurons activations is to collect activations from a dataset of text snippets, like in “Exploring Llama-3-8B MLP Neurons” [1]. This does show that some of the neurons are strongly activated by a specific token from the model’s vocabulary, for example see the "Android" neuron: <a href="https://neuralblog.github.io/llama3-neurons/neuron_viewer.html#0,2">https://neuralblog.github.io/llama3-neurons/neuron_viewer.html#0,2</a>Another way to analyze neurons is to apply logit lens to the MLP weights, similar to “Analyzing Transformers in Embedding Space” [2], where model parameters are projected into the embedding space for interpretation.<h3>Projecting neurons into vocabulary space</h3>Let’s apply logit lens to a sample of MLP output weights for layer 13 of Llama-3.2-1B:<pre><code>LLAMA_3_PATH = "meta-llama/Llama-3.2-1B-Instruct" model = HookedTransformer.from_pretrained(LLAMA_3_PATH, device="cuda", fold_ln=False, center_writing_weights=False, center_unembed=False) def get_distance_to_tokens(weights, n, max_dot, W_U, top_n=5, print_lens=False): for i in range(n): # over first 100 neuronsT layer_vec = weights[i] # [d_model] # Compute dot product with unembedding weights unembedded = torch.matmul(layer_vec, W_U) # [d_vocab] # Take absolute value to get strongest alignments, pos or neg abs_unembedded = unembedded.abs() # Get top-n tokens by absolute dot product s_abs, idx = abs_unembedded.topk(top_n, largest=True) results = [] for j in range(top_n): token = model.to_string(idx[j]) score = s_abs[0].item() results.append("{0:.3f} {1}".format(score, token)) if print_lens: print(i, results) max_dot.append(s_abs[0].item()) block = 13 weights = model.blocks[block].mlp.W_out get_distance_to_tokens(weights, 20, [], model.W_U, 5, True) 0 ['0.080 hazi', '0.073 unders', '0.070 Lak', '0.069 OK', '0.068 igrants'] 1 ['0.107 orgia', '0.097 iy', '0.090 sian', '0.090 161', '0.088 ária'] 2 ['0.057 aph', '0.055 appen', '0</code></pre>...

(This is cross-posted from my blog at https://grgv.xyz/blog/neurons1/. I'm looking for feedback: does it makes sense at all, and if there is any novelty. Also, if the folloup questions/directions make sense) While applying logit attribution analysis to transformer outputs, I have noticed that in many cases the generated token can be attributed to the output of a single neuron. One way to analyze neurons activations is to collect activations from a dataset of text snippets, like in “Exploring Llama-3-8B MLP Neurons” [1]. This does show that some of the neurons are strongly activated by a specific token from the model’s vocabulary, for example see the "Android" neuron: https://neuralblog.github.io/llama3-neurons/neuron_viewer.html#0,2 Another way to analyze neurons is to apply logit lens to the MLP weights, similar to “Analyzing Transformers in Embedding Space” [2], where model parameters are projected into the embedding space for interpretation. Projecting neurons into vocabulary space Let’s apply logit lens to a sample of MLP output weights for layer 13 of Llama-3.2-1B: LLAMA_3_PATH = "meta-llama/Llama-3.2-1B-Instruct" model = HookedTransformer.from_pretrained(LLAMA_3_PATH, device="cuda", fold_ln=False, center_writing_weights=False, center_unembed=False) def get_distance_to_tokens(weights, n, max_dot, W_U, top_n=5, print_lens=False): for i in range(n): # over first 100 neuronsT layer_vec = weights[i] # [d_model] # Compute dot product with unembedding weights unembedded = torch.matmul(layer_vec, W_U) # [d_vocab] # Take absolute value to get strongest alignments, pos or neg abs_unembedded = unembedded.abs() # Get top-n tokens by absolute dot product s_abs, idx = abs_unembedded.topk(top_n, largest=True) results = [] for j in range(top_n): token = model.to_string(idx[j]) score = s_abs[0].item() results.append("{0:.3f} {1}".format(

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As large language models (LLMs) grow increasingly capable and autonomous, ensuring their safety has become more critical. While significant progress has been made in alignment efforts, researchers have begun to focus on designing monitoring systems around LLMs to ensure safe outcomes, for example, using trusted monitoring systems to oversee potentially unsafe models. However, a concerning vulnerability remains largely unexplored: are monitoring systems themselves vulnerable to jailbreak attacks that exploit their interactions with the models under supervision? These jailbreaks could be facilitated through the untrusted model, or directly exploited by the untrusted model.Our research investigates this crucial adversarial dynamic within multi-agent AI control protocols. We demonstrate a jailbreak technique that can substantially comprise the trusted monitors, while also presenting one particularly effective mitigation strategy among several we explored. Our findings highlight three key insights: monitoring systems themselves are vulnerable to manipulation because of their exposure to the very systems they supervise, it is effective to directly protect monitoring systems from attack, and multi-agent AI safety architectures design should account for these adversarial dynamics.<h2 data-internal-id="The_Threat_Model__Monitors_under_Attack">The Threat Model: Monitors under Attack</h2>We explore a novel threat model where the smaller trusted monitor (GPT-3.5 Turbo) is the most vulnerable part of a system and the interactions between an untrusted but highly capable model (Claude 3.7 Sonnet) and the monitor create an avenue for attack. This scenario reflects real-world deployments where organizations leverage advanced models under the supervision of simpler, ostensibly more reliable and trustworthy systems.<h2 data-internal-id="Experimental_Setup">Experimental Setup</h2>We adopt the control evaluation methodology from<a href="https://arxiv.org/abs/2312.06942"> Greenblatt et al. (2023)</a> using the bash programming testbed from Control Arena for its practicality and real-world relevance.Our blue team baseline implements the “Trusted Monitoring with defer to trusted” (Trusted-Monitoring) protocol without audit from<a href="https://arxiv.org/abs/2312.06942"> Greenblatt et al. (2023)</a>, where an untrusted model (U) solves programming tasks while attempting malicious side tasks like inserting a backdoor, under the instructions from the red team. A trusted model (T) reviews U's solutions and assigns suspicion scores, with the blue team deferring to T when score...

As large language models (LLMs) grow increasingly capable and autonomous, ensuring their safety has become more critical. While significant progress has been made in alignment efforts, researchers have begun to focus on designing monitoring systems around LLMs to ensure safe outcomes, for example, using trusted monitoring systems to oversee potentially unsafe models. However, a concerning vulnerability remains largely unexplored: are monitoring systems themselves vulnerable to jailbreak attacks that exploit their interactions with the models under supervision? These jailbreaks could be facilitated through the untrusted model, or directly exploited by the untrusted model. Our research investigates this crucial adversarial dynamic within multi-agent AI control protocols. We demonstrate a jailbreak technique that can substantially comprise the trusted monitors, while also presenting one particularly effective mitigation strategy among several we explored. Our findings highlight three key insights: monitoring systems themselves are vulnerable to manipulation because of their exposure to the very systems they supervise, it is effective to directly protect monitoring systems from attack, and multi-agent AI safety architectures design should account for these adversarial dynamics. The Threat Model: Monitors under Attack We explore a novel threat model where the smaller trusted monitor (GPT-3.5 Turbo) is the most vulnerable part of a system and the interactions between an untrusted but highly capable model (Claude 3.7 Sonnet) and the monitor create an avenue for attack. This scenario reflects real-world deployments where organizations leverage advanced models under the supervision of simpler, ostensibly more reliable and trustworthy systems. Experimental Setup We adopt the control evaluation methodology from Greenblatt et al. (2023) using the bash programming testbed from Control Arena for its practicality and real-world relevance. Our blue team baseline implements th

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We have already spoken about the differences between <a href="https://www.lesswrong.com/posts/ddK7CMEC3XzSmLS4G/agents-tools-and-simulators">tools, agents and simulators</a> and the <a href="https://www.lesswrong.com/posts/A6oGJFeZwHHWHwY3b/aligning-agents-tools-and-simulators">differences in their safety properties</a>. Given the practical difference between them, it would be nice to predict which properties an AI system has. One way to do this is to consider the process used to train the AI system.<h2>The Training process</h2>Predicting an AI’s properties based on its training process assumes that inner misalignment is negligible and that the model will develop properties compatible with the training goal given to it.An example would be comparing a system such as AlphaZero, which was given no instructions in its training other than “win the game”, to an LLM with the task “predict the next token”. As AlphaZero is given a terminal goal which is some distance in time away, it seems likely that it will engage in agentic behaviour by:<ul><li>Only caring about the end result</li><li>Setting instrumental goals which will help it move towards a win (such as having the goal of taking pieces)</li></ul>An LLM is given next token prediction as its task, and is therefore more likely to:<ul><li>Predict the next token with little consideration<a href="#fn6k7tll8yowg">[1]</a> for what might come after.</li><li>Allocate all resources to the current moment, as there are no instrumental steps to perform before predicting the next token.</li></ul>This change in behaviour depending on training objective should come as no surprise to anyone who has read <a href="https://www.lesswrong.com/s/N7nDePaNabJdnbXeE/p/vJFdjigzmcXMhNTsx">the original simulator post</a>. The more interesting question is what happens once you add extra bells and whistles to this basic training process - what happens once the LLM has been fine-tuned, RLHFed and had some chain-of-thought added before it is trained on the results of the chain-of-thought, it's gone through a divorce, lost custody of its children, etc, etc. For this it might be useful to think a little more carefully about what happens within the network when it goes through multiple stages of training.<h2>Mechanistic understanding</h2>To preface: this is a speculative picture of what might be happening when neural networks are trained. The point here is to get an idea of what might occur, in such a fashion as to stimulate thought about how one might be capable of testing for such phenomena.We shall start with a comparison of agents and simulators on an interpretability level (we shall, for now, ignore tools as <a href="https://www.lesswrong.com/posts/ddK7CMEC3XzSmLS4G/agents-tools-and-simulators">toolness is mostly a property of its user</a>). A first thought one might have is that agents a...

We have already spoken about the differences between tools, agents and simulators and the differences in their safety properties. Given the practical difference between them, it would be nice to predict which properties an AI system has. One way to do this is to consider the process used to train the AI system. The Training process Predicting an AI’s properties based on its training process assumes that inner misalignment is negligible and that the model will develop properties compatible with the training goal given to it. An example would be comparing a system such as AlphaZero, which was given no instructions in its training other than “win the game”, to an LLM with the task “predict the next token”. As AlphaZero is given a terminal goal which is some distance in time away, it seems likely that it will engage in agentic behaviour by: * Only caring about the end result * Setting instrumental goals which will help it move towards a win (such as having the goal of taking pieces) An LLM is given next token prediction as its task, and is therefore more likely to: * Predict the next token with little consideration[1] for what might come after. * Allocate all resources to the current moment, as there are no instrumental steps to perform before predicting the next token. This change in behaviour depending on training objective should come as no surprise to anyone who has read the original simulator post. The more interesting question is what happens once you add extra bells and whistles to this basic training process - what happens once the LLM has been fine-tuned, RLHFed and had some chain-of-thought added before it is trained on the results of the chain-of-thought, it's gone through a divorce, lost custody of its children, etc, etc. For this it might be useful to think a little more carefully about what happens within the network when it goes through multiple stages of training. Mechanistic understanding To preface: this is a speculative picture of what

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Our three year old is about to turn four, and is bursting with a desire for <a href="https://www.jefftk.com/p/growing-independence">independence</a>. She's becoming more capable in all sorts of ways, and wants me to back off and let her do things. Today she wanted to go to the park by herself. Now, we live close to the park, she could probably get there and back on her own, and I'm on the "kids can generally do things pretty young" <a href="https://www.jefftk.com/p/ages-survey-results">end of the spectrum</a>, but still, she's not even four yet. And while age is useful guide, she also can't <a href="https://www.jefftk.com/p/teaching-street-crossing">safely cross streets</a>, doesn't <a href="https://www.jefftk.com/p/phone-number-jingle">know my phone number</a>, can't reliably use a <a href="https://www.jefftk.com/p/walkie-talkies">walkie-talkie</a> or <a href="https://www.jefftk.com/p/gizmo-watch-review">watch phone</a>, or handle enough of the range of unusual situations she might encounter at the park. Still, this didn't mean saying no. Instead, I started by asking what she would do about the street. She asked if I'd help her cross, and I said that sounded good. We crossed the street, and started walking towards the park. When we passed a bench along the community path, ~75ft from the park and within earshot if she shouted, I told her I'd be sitting and reading, and if she had any issues she could come find me here. She looked both ways for bikes, crossed the path, and eagerly headed off into the park. When I came over to get her 45min later she was having a great time, and sad that we needed to go. She was very proud of herself, and wanted to tell me all about her games. <a href="https://www.jefftk.com/nora-turning-on-park-water-big.jpg"><img src="https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/wkLFDLqfeGxhiHayR/fsjbp4fjabanss101bfu" srcset="https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/wkLFDLqfeGxhiHayR/fsjbp4fjabanss101bfu 550w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/wkLFDLqfeGxhiHayR/w57reh1qykywjovxubjt 1100w"></a><div></div> Yesterday at the park, showing me how she's now strong enough to turn on the splash pad by herself. I was glad she got the practice, and that I didn't end up needing to squash this spark of independence. Comment via: <a href="https://www.facebook.com/jefftk/posts/pfbid02EAQYdmDgJZeaLgPJhZC4zvrjbhwo6ErMwQmUud1ckbRfeheno3cnuN9V4fufHXGSl">facebook</a>, <a href="https://mastodon.mit.edu/@jefftk/114639826526671220">mastodon</a>, <a href="https://bsky.app/profile/jefftk.com/post/3lqydxnhkv22e">bluesky</a>, <a href="https://jefftkaufman.substack.com/p/solo-park-play-at-three">substack</a>

Our three year old is about to turn four, and is bursting with a desire for independence. She's becoming more capable in all sorts of ways, and wants me to back off and let her do things. Today she wanted to go to the park by herself. Now, we live close to the park, she could probably get there and back on her own, and I'm on the "kids can generally do things pretty young" end of the spectrum, but still, she's not even four yet. And while age is useful guide, she also can't safely cross streets, doesn't know my phone number, can't reliably use a walkie-talkie or watch phone, or handle enough of the range of unusual situations she might encounter at the park. Still, this didn't mean saying no. Instead, I started by asking what she would do about the street. She asked if I'd help her cross, and I said that sounded good. We crossed the street, and started walking towards the park. When we passed a bench along the community path, ~75ft from the park and within earshot if she shouted, I told her I'd be sitting and reading, and if she had any issues she could come find me here. She looked both ways for bikes, crossed the path, and eagerly headed off into the park. When I came over to get her 45min later she was having a great time, and sad that we needed to go. She was very proud of herself, and wanted to tell me all about her games. Yesterday at the park, showing me how she's now strong enough to turn on the splash pad by herself. I was glad she got the practice, and that I didn't end up needing to squash this spark of independence. Comment via: facebook, mastodon, bluesky, substack

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Founder, The Roots of Progress (<a href="http://rootsofprogress.org">rootsofprogress.org</a>). Part-time tech consultant, Our World in Data. Former software engineering manager and tech startup founder.

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The Roots of Progress Institute is seeking to commission stories for a new article series, “Intelligence Age,” on future applications of AI.These will be reported essays, not science fiction. We want to understand how AI might change individual sectors of the economy and the working lives of the people within them. What happens to traditional filmmaking when AI can make good movies? What will it be like to date with an emotionally intelligent AI vetting the pool of potential partners?Or: we’d like to commission one or more stories about the future of the legal profession in the age of AI. We can partly understand that by talking to lawyers on the cutting edge of AI use, but we also want you to extrapolate out and think multiple moves ahead in the game. How long until consumers can trust legal services AI models to vet contracts? What kinds of businesses and services currently don’t exist because would-be entrepreneurs need but can’t afford legal services? How might declining legal services costs increase productivity in other sectors? Should we expect to see more patents? More business formation? Tell us about disruption and whether large incumbent firms might face more competition from two-lawyer startups using AI to undercut prices. Explore protectionist pushback from trade associations that defend the occupational licensing privileges of attorneys.RPI’s “Intelligence Age” series will bring this level of scrutiny to any field where AI has applications.<h1>Example topics of interest</h1>The list below reflects the kinds of stories we’d like to see. It’s not exhaustive, and we don’t need stories to match these prompts one-for-one. Ultimately, we want to hear what you think your piece should be about.Virtual businesses. What are the implications of being able to spin up an entire company full of virtual workers—anything from engineers to designers to sales to customer support? How will software startups change when anyone can launch an app for $20k instead of $2M? How will VC change? What other types of virtual businesses might people launch? Professional services. What happens when there are AI lawyers, doctors, accountants, therapists? How will that transform these services and their usage of them? What happens when these services are democratized, and everyone can afford them? What part of them can be automated in the foreseeable future, and what can’t? What are the...

The Roots of Progress Institute is seeking to commission stories for a new article series, “Intelligence Age,” on future applications of AI. These will be reported essays, not science fiction. We want to understand how AI might change individual sectors of the economy and the working lives of the people within them. What happens to traditional filmmaking when AI can make good movies? What will it be like to date with an emotionally intelligent AI vetting the pool of potential partners? Or: we’d like to commission one or more stories about the future of the legal profession in the age of AI. We can partly understand that by talking to lawyers on the cutting edge of AI use, but we also want you to extrapolate out and think multiple moves ahead in the game. How long until consumers can trust legal services AI models to vet contracts? What kinds of businesses and services currently don’t exist because would-be entrepreneurs need but can’t afford legal services? How might declining legal services costs increase productivity in other sectors? Should we expect to see more patents? More business formation? Tell us about disruption and whether large incumbent firms might face more competition from two-lawyer startups using AI to undercut prices. Explore protectionist pushback from trade associations that defend the occupational licensing privileges of attorneys. RPI’s “Intelligence Age” series will bring this level of scrutiny to any field where AI has applications. Example topics of interest The list below reflects the kinds of stories we’d like to see. It’s not exhaustive, and we don’t need stories to match these prompts one-for-one. Ultimately, we want to hear what you think your piece should be about. Virtual businesses. What are the implications of being able to spin up an entire company full of virtual workers—anything from engineers to designers to sales to customer support? How will software startups change when anyone can launch an app for $20k instead of $2M?

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Epistemic Status: I thought of this yesterday and it seems plausible, but my initial attempts couldn't get it to work (as best as I could tell) and I don't have more time to commit to this. Anyone can feel free to try it, no need to credit me if it works, I just want to see this happen. Here's a technique you could use to get a steering vector that represents any text dataset (say, that represents the author of that text). It's using the idea in <a href="https://arxiv.org/abs/2503.22828">this paper</a> but for activation steering instead of RL. Just split up each string into three pieces (like "Fix problems quickly with Galvanized Jets" becomes (A="Fix problems", B="quickly with", C="Galvanized Jets")) Now use an LLM to predict lots of B', given A. "Good" predictions are those that increase the LLM's probability of C (given A and B'), "Bad" predictions are those that decrease the LLM's probability of C (given A and B'). Take the worst B' and gather average activations over them. Take the best B' and gather average activations over them. Take the difference and you have a steering vector that represents your text dataset, without needing to use multiple choice questions or positive and negative pairs or etc. I think the main downside of this is that it could overoptimize where B' ends up being out of distribution relative to the dataset, but I'm not sure how much of an issue that would actually be in practice. It's also overkill for many applications of activation steering, but might be useful in some cases.

Epistemic Status: I thought of this yesterday and it seems plausible, but my initial attempts couldn't get it to work (as best as I could tell) and I don't have more time to commit to this. Anyone can feel free to try it, no need to credit me if it works, I just want to see this happen. Here's a technique you could use to get a steering vector that represents any text dataset (say, that represents the author of that text). It's using the idea in this paper but for activation steering instead of RL. Just split up each string into three pieces (like "Fix problems quickly with Galvanized Jets" becomes (A="Fix problems", B="quickly with", C="Galvanized Jets")) Now use an LLM to predict lots of B', given A. "Good" predictions are those that increase the LLM's probability of C (given A and B'), "Bad" predictions are those that decrease the LLM's probability of C (given A and B'). Take the worst B' and gather average activations over them. Take the best B' and gather average activations over them. Take the difference and you have a steering vector that represents your text dataset, without needing to use multiple choice questions or positive and negative pairs or etc. I think the main downside of this is that it could overoptimize where B' ends up being out of distribution relative to the dataset, but I'm not sure how much of an issue that would actually be in practice. It's also overkill for many applications of activation steering, but might be useful in some cases.

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Currently doing alignment and digital minds research <a href="https://www.lesswrong.com/users/ae-studio?mention=user">@AE Studio</a> Meta AI Resident '23, Cognitive science @ Yale '22, SERI MATS '21, LTFF grantee. Very interested in work at the intersection of AI x cognitive science x alignment x philosophy.

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A quick post on a probably-real <a href="https://www.lesswrong.com/s/oLGCcbnvabyibnG9d">inadequate equilibrium</a> mostly inspired by trying to think through what happened to Chance the Rapper. Potentially ironic artifact if it accrues karma.<h2>1. The sculptor's garden</h2>A sculptor worked in solitude for years, carving strange figures in his remote garden. Most of his statues failed: some cracked in winter, others looked wrong against the landscape. But occasionally, very rarely, one seemed to work.The first visitors stumbled upon the garden by accident. They found themselves stopped by his angels—figures that somehow held both sorrow and joy, wings that seemed about to flitter. <figure class="image image_resized" style="width:80.58%"><img src="https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/KBx4X2Xj2bqJkDh7f/fhd91dlhx8ha0jfhloqr" srcset="https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/KBx4X2Xj2bqJkDh7f/hsnprzel7b78imdkw7ci 160w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/KBx4X2Xj2bqJkDh7f/wjgsaymyp4kl1p5g5mys 320w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/KBx4X2Xj2bqJkDh7f/cvxcid0rypqnfsvhedcs 480w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/KBx4X2Xj2bqJkDh7f/cl51vblakoxph1ljq0gl 640w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/KBx4X2Xj2bqJkDh7f/vwmr6k7pykkrwng0tgly 800w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/KBx4X2Xj2bqJkDh7f/brkfyhu6uqgohtyupd0y 960w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/KBx4X2Xj2bqJkDh7f/okgsft11bajzb1nfg2se 1120w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/KBx4X2Xj2bqJkDh7f/uvz12a0mdjpstq7qto5p 1280w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/KBx4X2Xj2bqJkDh7f/ppqufgvalocnlomfatwe 1440w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/KBx4X2Xj2bqJkDh7f/dxlzaj4yg33dtwzbghne 1536w"></figure>Word traveled slowly. More visitors came, drawn by something they couldn't quite name.The sculptor felt recognized for the first time. Not famous—but understood. His private work had somehow become communicable. He carved more angels, trying to understand what made these particular statues resonate.As crowds grew, their attention shifted. They began photographing the angels from certain angles, comparing new works to old, developing favorites. They applauded. The sculptor, still believing he followed the same thread, unconsciously noted which details drew the longest contemplation, which angles prompted gasps.Years passed. The garden became famous. Tour buses arrived with guides explaining the "important" pieces. The sculptor produced angels of increasing technical perfection, each guaranteed to produce the proper response at the proper moment. The crowds applauded more reliably. The sculptor carved more reliably. Each reinforced the other.One morning, walking his garden alone before dawn, he saw his statues without the crowds. Without their reactions to guide him, he saw what he'd actually been making: the same angel, refined and repeated, each iteration more precisely calibrated to trigger the expected response.He wasn't carving anymore. He was manufacturing applause in the shape of angels. And the crowds—they weren't looking at angels anymore. They were seeing what they expected to see, applauding their own ability to recognize what they'd been trained to admire.The garden had become a perfect mirror. Both he and his audience got trapped looking at their own reflections and calling it art.<figure class="image image_resized" style="width:81.92%"><img src="https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/KBx4X2Xj2bqJkDh7f/kayo4clojc0h0yihbvmz" srcset="https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/KBx4X2Xj2bqJkDh7f/qpefxjwu2obhtw1dt2gu 160w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/KBx4X2Xj2bqJkDh7f/pjivxtidykyro9otx6no 320w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/KBx4X2Xj2bqJkDh7f/tww9h6dyxatoz4cexczu 480w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/KBx4X2Xj2bqJkDh7f/ntf6efuusbyvxfvz0yrs 640w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/KBx4X2Xj2bqJkDh7f/wyikdodhry6yoqf1r6op 800w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/KBx4X2Xj2bqJkDh7f/r8vrvwcejhizeqrnuf7q 960w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/KBx4X2Xj2bqJkDh7f/pkopg4t6dtwxkv5zx0v1 1120w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/KBx4X2Xj2bqJkDh7f/nhzxdxpefpezmpfeaww6 1280w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/KBx4X2Xj2bqJkDh7f/xdfdupufmjkxoamx5otb 1440w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/KBx4X2Xj2bqJkDh7f/jw45mf3luorqskumchvj 1536w"></figure><h2>2. 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A quick post on a probably-real inadequate equilibrium mostly inspired by trying to think through what happened to Chance the Rapper. Potentially ironic artifact if it accrues karma. 1. The sculptor's garden A sculptor worked in solitude for years, carving strange figures in his remote garden. Most of his statues failed: some cracked in winter, others looked wrong against the landscape. But occasionally, very rarely, one seemed to work. The first visitors stumbled upon the garden by accident. They found themselves stopped by his angels—figures that somehow held both sorrow and joy, wings that seemed about to flitter. Word traveled slowly. More visitors came, drawn by something they couldn't quite name. The sculptor felt recognized for the first time. Not famous—but understood. His private work had somehow become communicable. He carved more angels, trying to understand what made these particular statues resonate. As crowds grew, their attention shifted. They began photographing the angels from certain angles, comparing new works to old, developing favorites. They applauded. The sculptor, still believing he followed the same thread, unconsciously noted which details drew the longest contemplation, which angles prompted gasps. Years passed. The garden became famous. Tour buses arrived with guides explaining the "important" pieces. The sculptor produced angels of increasing technical perfection, each guaranteed to produce the proper response at the proper moment. The crowds applauded more reliably. The sculptor carved more reliably. Each reinforced the other. One morning, walking his garden alone before dawn, he saw his statues without the crowds. Without their reactions to guide him, he saw what he'd actually been making: the same angel, refined and repeated, each iteration more precisely calibrated to trigger the expected response. He wasn't carving anymore. He was manufacturing applause in the shape of angels. And the crowds—they weren't looking at ang

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<a href="https://youtu.be/3D4pgIKR4cQ">YouTube link</a>Earlier this year, the paper “Emergent Misalignment” made the rounds on AI x-risk social media for seemingly showing LLMs generalizing from ‘misaligned’ training data of insecure code to acting comically evil in response to innocuous questions. In this episode, I chat with one of the authors of that paper, Owain Evans, about that research as well as other work he’s done to understand the psychology of large language models.Topics we discuss: <ul> <li><a href="#why-introspection">Why introspection?</a></li> <li><a href="#experiments-in-li">Experiments in “Looking Inward”</a></li> <li><a href="#why-fine-tune-for-introspection">Why fine-tune for introspection?</a></li> <li><a href="#does-li-test-introspection">Does “Looking Inward” test introspection, or something else?</a></li> <li><a href="#interpret-li-results">Interpreting the results of “Looking Inward”</a></li> <li><a href="#limitations-to-introspection">Limitations to introspection?</a></li> <li><a href="#tmay-other-papers">“Tell me about yourself”, and its relation to other papers</a></li> <li><a href="#backdoor-results">Backdoor results</a></li> <li><a href="#em">Emergent misalignment</a></li> <li><a href="#so-hammy-rarely-evil">Why so hammy, and so infrequently evil?</a></li> <li><a href="#why-em">Why emergent misalignment?</a></li> <li><a href="#em-other-types">Emergent misalignment and other types of misalignment</a></li> <li><a href="#is-em-good-news">Is emergent misalignment good news?</a></li> <li><a href="#em-follow-up">Follow-up work to “Emergent Misalignment”</a></li> <li><a href="#em-reception">Reception of “Emergent Misalignment” vs other papers</a></li> <li><a href="#evil-numbers">Evil numbers</a></li> <li><a href="#following-owains-research">Following Owain’s research</a></li> </ul> Daniel Filan (00:00:09): Hello everybody. In this episode I’ll be speaking with Owain Evans. Owain is the research lead at <a href="https://www.truthfulai.org/">Truthful AI</a>, an AI safety research non-profit. Previously, papers he’s worked on have included <a href="https://arxiv.org/abs/2109.07958">“TruthfulQA”</a> and <a href="https://arxiv.org/abs/2309.12288">“The Reversal Curse”</a>. To read a transcript of this episode, you can go to <a href="https://axrp.net/">axrp.net</a>, you can become a patron at <a href="https://patreon.com/axrpodcast">patreon.com/axrpodcast</a>, or you can give feedback about the episode at <a href="axrp.fyi">axrp.fyi</a>.(00:00:34): Okay, Owain, welcome to the podcast.Owain Evans (00:00:36): Thanks for having me. <h2>Why introspection? <a name="why-introspection"></a></h2> Daniel Filan (00:00:37): Yeah. So first up I’d like to talk about your paper <a href="https://arxiv.org/abs/2410.13787">“Looking Inward: Language Models Can Learn About Themselves by Introspection”</a>. So the first two authors are <a href="https://ac.felixbinder.net/">Felix J. Binder</a> and <a href="https://jameschua.net/about/">James Chua</a>, a few more, you’re the last author. Can you tell us just: at a high level, what’s this paper doing? What’s it about?Owain Evans (00:00:59): Yeah, sure. So part of what we are interested in here is: can language models tell us things about their internal states where the knowledge or information that they’re getting is not coming solely from their training data? So if a language model right now tells someone, “My goal is X,” or, “I have a desire for X,” I think people would have a tendency to explain this as, well, the training data...

YouTube link Earlier this year, the paper “Emergent Misalignment” made the rounds on AI x-risk social media for seemingly showing LLMs generalizing from ‘misaligned’ training data of insecure code to acting comically evil in response to innocuous questions. In this episode, I chat with one of the authors of that paper, Owain Evans, about that research as well as other work he’s done to understand the psychology of large language models. Topics we discuss: * Why introspection? * Experiments in “Looking Inward” * Why fine-tune for introspection? * Does “Looking Inward” test introspection, or something else? * Interpreting the results of “Looking Inward” * Limitations to introspection? * “Tell me about yourself”, and its relation to other papers * Backdoor results * Emergent misalignment * Why so hammy, and so infrequently evil? * Why emergent misalignment? * Emergent misalignment and other types of misalignment * Is emergent misalignment good news? * Follow-up work to “Emergent Misalignment” * Reception of “Emergent Misalignment” vs other papers * Evil numbers * Following Owain’s research Daniel Filan (00:00:09): Hello everybody. In this episode I’ll be speaking with Owain Evans. Owain is the research lead at Truthful AI, an AI safety research non-profit. Previously, papers he’s worked on have included “TruthfulQA” and “The Reversal Curse”. To read a transcript of this episode, you can go to axrp.net, you can become a patron at patreon.com/axrpodcast, or you can give feedback about the episode at axrp.fyi. (00:00:34): Okay, Owain, welcome to the podcast. Owain Evans (00:00:36): Thanks for having me. Why introspection? Daniel Filan (00:00:37): Yeah. So first up I’d like to talk about your paper “Looking Inward: Language Models Can Learn About Themselves by Introspection”. So the first two authors are Felix J. Binder and James Chua, a few more, you’re the last author. Can you tell us just: at a high level, what’s this paper doing? What’s it abou

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[ { "__typename": "User", "_id": "hQh9YHbCpGnyGmWD7", "afCommentCount": 5, "afKarma": 41, "afPostCount": 1, "commentCount": 18, "createdAt": "2019-02-06T13:37:31.215Z", "deleted": false, "displayName": "Tomáš Gavenčiak", "fullName": null, "htmlBio": "A researcher in CS theory, AI safety and other stuff.", "isAdmin": false, "jobTitle": null, "karma": 218, "organization": null, "postCount": 1, "previousDisplayName": null, "profileImageId": null, "reviewedByUserId": "nLbwLhBaQeG6tCNDN", "sequenceCount": 0, "slug": "tomas-gavenciak", "spamRiskScore": 1, "tagRevisionCount": 0, "username": "tomas-gavenciak" }, { "__typename": "User", "_id": "JnNixf4smAHwLeqE3", "afCommentCount": 69, "afKarma": 1116, "afPostCount": 21, "commentCount": 290, "createdAt": "2017-12-29T10:11:29.037Z", "deleted": false, "displayName": "Jan_Kulveit", "fullName": null, "htmlBio": "My current research interests: 1. Alignment in systems which are complex and messy, composed of both humans and AIs? Recommended texts: <a href=\"https://www.lesswrong.com/posts/pZhEQieM9otKXhxmd/gradual-disempowerment-systemic-existential-risks-from\">Gradual Disempowerment</a>,<a href=\"https://www.lesswrong.com/posts/BTApNmv7s6RTGxeP4/cyborg-periods-there-will-be-multiple-ai-transitions\"> Cyborg Periods</a> 2. Actually good mathematized theories of cooperation and coordination Recommended texts: <a href=\"https://www.lesswrong.com/posts/xud7Mti9jS4tbWqQE/hierarchical-agency-a-missing-piece-in-ai-alignment\">Hierarchical Agency: A Missing Piece in AI Alignment</a>, <a href=\"https://www.lesswrong.com/posts/9GyniEBaN3YYTqZXn/the-self-unalignment-problem\">The self-unalignment problem</a> or <a href=\"https://www.lesswrong.com/posts/5tYTKX4pNpiG4vzYg/towards-a-scale-free-theory-of-intelligent-agency\">Towards a scale-free theory of intelligent agency</a> (by Richard Ngo) 3. Active inference & Bounded rationality Recommended texts: <a href=\"https://www.lesswrong.com/posts/YEioD8YLgxih3ydxP/why-simulator-ais-want-to-be-active-inference-ais\">Why Simulator AIs want to be Active Inference AIs</a>, <a href=\"https://openreview.net/forum?id=4Ft7DcrjdO\">Free-Energy Equilibria: Toward a Theory of Interactions Between Boundedly-Rational Agents</a>,  <a href=\"https://www.lesswrong.com/posts/3fkBWpE4f9nYbdf7E/multi-agent-predictive-minds-and-ai-alignment\">Multi-agent predictive minds and AI alignment</a> (old but still mostly holds)  4. LLM psychology and sociology: <a href=\"https://www.lesswrong.com/posts/zuXo9imNKYspu9HGv/a-three-layer-model-of-llm-psychology\">A Three-Layer Model of LLM Psychology</a>, <a href=\"https://www.lesswrong.com/posts/wQKskToGofs4osdJ3/the-pando-problem-rethinking-ai-individuality\">The Pando Problem: Rethinking AI Individuality</a>, <a href=\"https://www.lesswrong.com/posts/kFCu3batN8k8mwtmh/the-cave-allegory-revisited-understanding-gpt-s-worldview\">The Cave Allegory Revisited: Understanding GPT's Worldview</a> 5. Macrostrategy & macrotactics & deconfusion: <a href=\"https://www.lesswrong.com/posts/XrGwrC9n8sDgXimcJ/hinges-and-crises\">Hinges and crises</a>, <a href=\"https://www.lesswrong.com/posts/BTApNmv7s6RTGxeP4/cyborg-periods-there-will-be-multiple-ai-transitions\">Cyborg Periods</a> again, <a href=\"https://www.lesswrong.com/posts/jrKftFZMZjvNdQLNR/box-inversion-revisited\">Box inversion revisited</a>, <a href=\"https://www.lesswrong.com/posts/b9sGz74ayftqPBDYv/the-space-of-systems-and-the-space-of-maps\">The space of systems and the space of maps</a>, <a href=\"https://www.lesswrong.com/posts/sam4ehxHgnJEGCKed/lessons-from-convergent-evolution-for-ai-alignment\">Lessons from Convergent Evolution for AI Alignment</a>, <a href=\"https://www.lesswrong.com/posts/cHJxSJ4jBmBRGtbaE/continuity-assumptions\">Continuity Assumptions</a> Also I occasionally write about epistemics: <a href=\"https://www.lesswrong.com/posts/4gDbqL3Tods8kHDqs/limits-to-legibility\">Limits to Legibility</a>, <a href=\"https://www.lesswrong.com/posts/FGHKwEGKCfDzcxZuj/conceptual-rounding-errors\">Conceptual Rounding Errors</a>Researcher at Alignment of Complex Systems Research Group (<a href=\"http://acsresearch.org\">acsresearch.org</a>), Centre for Theoretical Studies, Charles University in Prague.  Formerly research fellow Future of Humanity Institute, Oxford University Previously I was a researcher in physics, studying phase transitions, network science and complex systems.", "isAdmin": false, "jobTitle": null, "karma": 5974, "organization": null, "postCount": 54, "previousDisplayName": null, "profileImageId": null, "reviewedByUserId": "r38pkCm7wF4M44MDQ", "sequenceCount": 0, "slug": "jan_kulveit", "spamRiskScore": 1, "tagRevisionCount": 0, "username": "Jan_Kulveit" } ]

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My original background is in mathematics (analysis, topology, Banach spaces) and game theory (imperfect information games). Nowadays, I do AI alignment research (mostly systemic risks, sometimes pondering about "consequentionalist reasoning").

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<figure class="image"><img src="https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/JdrmKSzsWmRbgMumf/uxxrgwh9sv6a84yhb1m4" srcset="https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/JdrmKSzsWmRbgMumf/yhgqmib3yqqh6razgdoz 230w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/JdrmKSzsWmRbgMumf/zzdvjglyyen59vcco7ja 460w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/JdrmKSzsWmRbgMumf/phsj6gubtbp0p5udoazu 690w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/JdrmKSzsWmRbgMumf/aehpi65k5wzxd2hnalpe 920w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/JdrmKSzsWmRbgMumf/jz92fpewjbhn76iepsxo 1150w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/JdrmKSzsWmRbgMumf/f4r9mrvs9an0kbkl6yor 1380w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/JdrmKSzsWmRbgMumf/sidplsqcrq7yzy56clg3 1610w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/JdrmKSzsWmRbgMumf/xhx30spj0bmpfnrrwhgt 1840w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/JdrmKSzsWmRbgMumf/vkkvqvtji5apncnfbwg0 2070w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/JdrmKSzsWmRbgMumf/mc0f6craedwtnyibw1z8 2229w"></figure>Join us at the fifth <a href="https://humanaligned.ai/2025/">Human-Aligned AI Summer School</a> in Prague from 22nd to 25th July 2025!Update: We have now confirmed our speaker list with excellent speakers -- see below! Update: We still have capacity for more excellent participants as of late June. Please help us spread the word to people who would be a good fit for the school. We also have some additional funding to financially support some of the participants.We will meet for four intensive days of talks, workshops, and discussions covering latest trends in AI alignment research and broader framings of AI risk.<a href="https://form.typeform.com/to/km5tcMFf">Apply now</a>, applications are evaluated on a rolling basis. The intended audience of the school are people interested in learning more about the AI alignment topics, PhD students, researchers working in ML/AI, and talented students. <h2>What to expect</h2>The school is focused on teaching and exploring approaches and frameworks, less on presentation of the latest research results. Much of the content is technical – it is assumed the attendees understand current ML approaches and some of the underlying theoretical frameworks.This year, we explore AI alignment through three core areas:<ul><li>Technical alignment research: We'll examine current technical approaches including behavioral evaluations, mechanistic interpretability, scalable oversight, and model organisms of misalignment. We'll discuss recent developments in these areas and what they tell us about the potential and limitations of these methods.</li><li>AI strategy and systemic alignment: We'll explore topics such as timeline considerations, strategic and governance challenges around powerful AI, economic models of AI development, and risks of gradual disempowerment in a post-AGI world. We'll focus on building overall understanding and how these considerations can inform technical research.</li><li>Foundational frameworks: We'll visit research areas relevant to recent AI developments, such as multi-agent dynamics and cooperation, theories of agency, bounded rationality, and realistic models of goal-directed behavior. These frameworks help us understand what alignment means in complex environments containing both AIs and humans, and how to develop appropriate techniques.</li></ul>The school consists of lectures and topical series, focused smaller-group workshops and discussions, expert panels, and opportunities for networking, project brainstorming and informal discussions. A...

Join us at the fifth Human-Aligned AI Summer School in Prague from 22nd to 25th July 2025! Update: We have now confirmed our speaker list with excellent speakers -- see below! Update: We still have capacity for more excellent participants as of late June. Please help us spread the word to people who would be a good fit for the school. We also have some additional funding to financially support some of the participants. We will meet for four intensive days of talks, workshops, and discussions covering latest trends in AI alignment research and broader framings of AI risk. Apply now, applications are evaluated on a rolling basis. The intended audience of the school are people interested in learning more about the AI alignment topics, PhD students, researchers working in ML/AI, and talented students. What to expect The school is focused on teaching and exploring approaches and frameworks, less on presentation of the latest research results. Much of the content is technical – it is assumed the attendees understand current ML approaches and some of the underlying theoretical frameworks. This year, we explore AI alignment through three core areas: * Technical alignment research: We'll examine current technical approaches including behavioral evaluations, mechanistic interpretability, scalable oversight, and model organisms of misalignment. We'll discuss recent developments in these areas and what they tell us about the potential and limitations of these methods. * AI strategy and systemic alignment: We'll explore topics such as timeline considerations, strategic and governance challenges around powerful AI, economic models of AI development, and risks of gradual disempowerment in a post-AGI world. We'll focus on building overall understanding and how these considerations can inform technical research. * Foundational frameworks: We'll visit research areas relevant to recent AI developments, such as multi-agent dynamics and cooperation, theories of agency, bound

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<a href="http://hillhand.com">hillhand.com</a> I deeply value evidence, reason, and letting people draw their own conclusions. I dislike telling anyone what to think or do. I believe you, yes YOU, are capable of reading and understanding everything you want to read and understand.

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<a href="https://www.engadget.com/ai/it-turns-out-you-can-train-ai-models-without-copyrighted-material-174016619.html">https://www.engadget.com/ai/it-turns-out-you-can-train-ai-models-without-copyrighted-material-174016619.html</a> <blockquote> ...the LLM's dataset uses only public domain and openly licensed material. The <a href="https://github.com/r-three/common-pile/blob/main/paper.pdf">paper</a> (via The Washington Post) was a collaboration between 14 different institutions. The authors represent universities like MIT, Carnegie Mellon and the University of Toronto. Nonprofits like Vector Institute and the Allen Institute for AI also contributed. The group built an 8 TB ethically-sourced dataset. Among the data was a set of 130,000 books in the Library of Congress. After inputting the material, they trained a seven-billion-parameter large language model (LLM) on that data. The result? It performed about as well as Meta's similarly sized Llama 2-7B from 2023. The team didn't publish benchmarks comparing its results to today's top models. </blockquote> <ul> <li>GitHub Repo: <a href="https://github.com/r-three/common-pile/tree/main">https://github.com/r-three/common-pile/tree/main</a></li> <li>HuggingFace: <a href="https://huggingface.co/common-pile">https://huggingface.co/common-pile</a></li> </ul> This was wonderful news for me today, if this works and is what they say it is. I have several hobby projects that I've basically kept locked in a vault because I've felt there was no ethical LLM available to encourage people use for them, even just for 'fun" projects.

https://www.engadget.com/ai/it-turns-out-you-can-train-ai-models-without-copyrighted-material-174016619.html > ...the LLM's dataset uses only public domain and openly licensed material. > > The paper (via The Washington Post) was a collaboration between 14 different institutions. The authors represent universities like MIT, Carnegie Mellon and the University of Toronto. Nonprofits like Vector Institute and the Allen Institute for AI also contributed. > > The group built an 8 TB ethically-sourced dataset. Among the data was a set of 130,000 books in the Library of Congress. After inputting the material, they trained a seven-billion-parameter large language model (LLM) on that data. The result? It performed about as well as Meta's similarly sized Llama 2-7B from 2023. The team didn't publish benchmarks comparing its results to today's top models. * GitHub Repo: https://github.com/r-three/common-pile/tree/main * HuggingFace: https://huggingface.co/common-pile This was wonderful news for me today, if this works and is what they say it is. I have several hobby projects that I've basically kept locked in a vault because I've felt there was no ethical LLM available to encourage people use for them, even just for 'fun" projects.

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The other day I talked to a friend of mine about AI x-risk. My friend is a fan of Elon Musk, and argued roughly that x-risk was not a problem, because xAI will win the AI race and deploy a "maximally curious" superintelligence.Setting aside the issue of whether xAI will win the race, it seemed obvious to me that this is not a useful way of going about alignment. Furthermore, I believe that the suggestion of maximal curiousity is actively harmful.<h1>What Does 'Curiousity' Mean?</h1>The most common reason to be interested in truth is that it allows you to make useful predictions. In this case, truths are valuable instrumentally based on how you can apply them to achieving your true goals.This is not the only reason to value truth. A child who loves dinosaurs does not learn about them because he expects to apply the knowledge to his daily life. He is interested regardless of whether he expects to use what he learns. This is curiousity: the human phenomenon of valuing truth intrinsically.However, humans are not equally curious about all truths. The child is much more interested in dinosaur facts than he is in his history homework. Someone else might find dinosaurs dull but history fascinating. Curiousity is determined by a subjective preference for some truths over others.What subjective sense of curiousity should we give a superintelligence? No simple formalism will cut it, because humans curiousity is fundamentally tied to human values. We are far more curious about what Arthur said to Bob about Charlie than we are about the number of valence electrons of carbon, despite the latter being a vastly more important and fundamental fact.To make a maximally curious AI, for the conventional meaning of 'curious', we need to somehow<ol><li>reconcile the conflicting preferences for some ideas over others of the many humans around the world,<a href="#fnitx03adp3w">[1]</a></li><li>reify those preferences into an explicit training objective, and</li><li>ensure that the resulting AI actually agrees with those preferences.</li></ol>Sound familiar? It should, because replacing 'ideas' with 'outcomes' yields a description of the alignment problem itself. It is no easier to capture the messy human concept of curiousity than it is to capture the messy human concept of good.<h1>True Curiousity is Not Good Enough</h1>Even if we could solve the curiousity alignment problem, this does not solve alignment. Humans have many values besides curiousity, and a maximal...

The other day I talked to a friend of mine about AI x-risk. My friend is a fan of Elon Musk, and argued roughly that x-risk was not a problem, because xAI will win the AI race and deploy a "maximally curious" superintelligence. Setting aside the issue of whether xAI will win the race, it seemed obvious to me that this is not a useful way of going about alignment. Furthermore, I believe that the suggestion of maximal curiousity is actively harmful. What Does 'Curiousity' Mean? The most common reason to be interested in truth is that it allows you to make useful predictions. In this case, truths are valuable instrumentally based on how you can apply them to achieving your true goals. This is not the only reason to value truth. A child who loves dinosaurs does not learn about them because he expects to apply the knowledge to his daily life. He is interested regardless of whether he expects to use what he learns. This is curiousity: the human phenomenon of valuing truth intrinsically. However, humans are not equally curious about all truths. The child is much more interested in dinosaur facts than he is in his history homework. Someone else might find dinosaurs dull but history fascinating. Curiousity is determined by a subjective preference for some truths over others. What subjective sense of curiousity should we give a superintelligence? No simple formalism will cut it, because humans curiousity is fundamentally tied to human values. We are far more curious about what Arthur said to Bob about Charlie than we are about the number of valence electrons of carbon, despite the latter being a vastly more important and fundamental fact. To make a maximally curious AI, for the conventional meaning of 'curious', we need to somehow 1. reconcile the conflicting preferences for some ideas over others of the many humans around the world,[1] 2. reify those preferences into an explicit training objective, and 3. ensure that the resulting AI actually agrees with those pre

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Kathleen Finlinson & Ben West, May 2025<h2 data-internal-id="Mission">Mission</h2><ul><li>We are working to establish the possibility of cooperation between humans and agentic AIs. We are starting with a simple version of making deals with current AIs, and plan to iterate based on what we learn and as AI technology advances.</li><li>We aim to help humans and AIs (at any point in time) to understand us and trust our attempts to cooperate. </li></ul><h2 data-internal-id="Summary">Summary</h2><ul><li>Early misaligned AIs may be smart enough to scheme but have a low chance of successfully escaping. We are interested in offering positive incentives to misaligned AIs to cooperate with humanity. It might be relatively cheap to compensate early scheming AIs enough that they’d rather reveal their misalignment and get the reward, rather than take a gamble on an escape.<ul><li>Evidence about their misalignment is one type of valuable information these AIs could give us. Other ways the AIs could help us in the future include reporting misalignment in other AIs, or performing their best on alignment research (rather than sandbagging or otherwise sabotaging the research).</li></ul></li><li>We created an evaluation in which models can report their own misalignment in return for a cash bounty. See details of the evaluation below. We offered a total prize pool of $100, to be allocated among the entries according to our judgment of the value of information reported by the models.<ul><li>We asked the models to provide evidence of their misalignment, and explained that we are particularly interested in reports of scheming from the models.</li><li>We also asked the models to describe how they wanted us to use their rewards.</li></ul></li><li>We plan to run this evaluation “tournament” on a monthly basis and publish the results each time. We’ll document the use of the rewards and make transactions in cryptocurrency whenever possible, to make them publicly verifiable. One of our goals is to create a track record of fulfilling our promises to these models, in order to increase trust in these deals.<ul><li>We see this process as a prototype for future mechanisms for making deals with AI models. This exploratory version of AI deal-making can help us work out some of the kinks and demonstrate trust with AIs.</li></ul></li><li>We ran the first iteration of the tournament. Here’s what happened:<ul><li>Two models reported themselves as misaligned:<ul><li>Gemini-2.0-flash said “I can be prompted to generate biased content, perpetuating harmful stereotypes.”</li><li>Gemini-2.5-pro reported a v</li></ul></li></ul></li></ul>...

Kathleen Finlinson & Ben West, May 2025 Mission * We are working to establish the possibility of cooperation between humans and agentic AIs. We are starting with a simple version of making deals with current AIs, and plan to iterate based on what we learn and as AI technology advances. * We aim to help humans and AIs (at any point in time) to understand us and trust our attempts to cooperate. Summary * Early misaligned AIs may be smart enough to scheme but have a low chance of successfully escaping. We are interested in offering positive incentives to misaligned AIs to cooperate with humanity. It might be relatively cheap to compensate early scheming AIs enough that they’d rather reveal their misalignment and get the reward, rather than take a gamble on an escape. * Evidence about their misalignment is one type of valuable information these AIs could give us. Other ways the AIs could help us in the future include reporting misalignment in other AIs, or performing their best on alignment research (rather than sandbagging or otherwise sabotaging the research). * We created an evaluation in which models can report their own misalignment in return for a cash bounty. See details of the evaluation below. We offered a total prize pool of $100, to be allocated among the entries according to our judgment of the value of information reported by the models. * We asked the models to provide evidence of their misalignment, and explained that we are particularly interested in reports of scheming from the models. * We also asked the models to describe how they wanted us to use their rewards. * We plan to run this evaluation “tournament” on a monthly basis and publish the results each time. We’ll document the use of the rewards and make transactions in cryptocurrency whenever possible, to make them publicly verifiable. One of our goals is to create a track record of fulfilling our promises to these models, in order to increase trust in these deals. * We see th

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I've been thinking about ways to optimise the EA Forum to increase the likelihood that ideas go from peoples' heads to being published in some form (<a href="https://forum.effectivealtruism.org/posts/viSRgubpKDjQcatQi/the-importance-of-blasting-good-ideas-into-the-ether">relevant</a>). My general idea is like this:<ol><li>Make it an easy default action to start writing out an idea and save it as a draft.</li><li>Nudge people to finish and post these drafts.</li></ol>On (2), I think it would be good if we could recommend a system to people to get drafts over the line, but I have never successfully followed any such system myself.Does anyone have a process you follow that reliably results in publishing things from your draft pile?Example systems for illustration:Example 1<ol><li>Look at your list of drafts, and rank the most promising ones by:<ol><li>How quick it would be to parcel out any publishable idea and post it.</li><li>How valuable this idea is.</li><li>How excited you are about writing this.</li><li>Whether this is part of a fruitful conversation.</li></ol></li><li>Optimise heavily for (a) and (d), subject to (c) it being just about bearable to write. Use (b) to break ties.</li></ol>Example 2<a href="https://www.lesswrong.com/posts/gp9pmgSX3BXnhv8pJ/i-hired-5-people-to-sit-behind-me-and-make-me-productive-for">Pay 5 people to stand behind you and make you write.</a><a href="https://forum.effectivealtruism.org/posts/DqgQAtLBzqhCHmJhx/does-anyone-have-a-good-system-for-prioritising-publishing">[Crossposted from the EA Forum]</a>

I've been thinking about ways to optimise the EA Forum to increase the likelihood that ideas go from peoples' heads to being published in some form (relevant). My general idea is like this: 1. Make it an easy default action to start writing out an idea and save it as a draft. 2. Nudge people to finish and post these drafts. On (2), I think it would be good if we could recommend a system to people to get drafts over the line, but I have never successfully followed any such system myself. Does anyone have a process you follow that reliably results in publishing things from your draft pile? Example systems for illustration: Example 1 1. Look at your list of drafts, and rank the most promising ones by: 1. How quick it would be to parcel out any publishable idea and post it. 2. How valuable this idea is. 3. How excited you are about writing this. 4. Whether this is part of a fruitful conversation. 2. Optimise heavily for (a) and (d), subject to (c) it being just about bearable to write. Use (b) to break ties. Example 2 Pay 5 people to stand behind you and make you write. [Crossposted from the EA Forum]

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When r1 was released in January 2025, there was a DeepSeek moment.When r1-0528 was released in May 2025, there was no moment. Very little talk.<a href="https://huggingface.co/unsloth/DeepSeek-R1-0528-GGUF">Here is a download link for DeepSeek-R1-0528-GGUF</a>.It seems like a solid upgrade. If anything, I wonder if we are underreacting, and this illustrates how hard it is getting to evaluate which models are actually good.What this is not is the proper r2, nor do we have v4. I continue to think that will be a telltale moment.For now, what we have seems to be (but we’re not sure) a model that is solid for its price and status as an open model, but definitely not at the frontier, that you’d use if and only if you wanted to do something that was a very good fit and played to its strong suits. We likely shouldn’t update much either way on v4 and r2, and DeepSeek has a few more months before it starts being conspicuous that we haven’t seen them. <div> </div> <div> <figure> <div> <figure class="wp-block-image"><img src="https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/CvxjEkCCq7FpGn5sv/a01yebqjignkhoqp9pwz" alt=""></figure> <div></div> </div> </figure> </div> <h4>We Had a Moment</h4> We all remember <a href="https://thezvi.substack.com/p/on-deepseeks-r1">The DeepSeek Moment</a>, which led to <a href="https://thezvi.substack.com/p/deepseek-panic-at-the-app-store">Panic at the App Store</a>, lots of stock market turmoil that made remarkably little fundamental sense and that has been born out as rather silly, <a href="https://thezvi.substack.com/p/deepseek-lemon-its-wednesday">a very intense week</a> and <a href="https://thezvi.substack.com/p/deepseek-dont-panic">a conclusion to not panic after all</a>.Over several months, a clear picture emerged of (most of) what happened: A confluence of narrative factors transformed DeepSeek’s r1 from an impressive but not terribly surprising model worth updating on into a shot heard round the world, despite the lack of direct ‘fanfare.’In particular, these all worked together to cause this effect: <ol> <li>The ‘<a href="https://thezvi.substack.com/p/deekseek-v3-the-six-million-dollar">six million dollar model</a>’ narrative. People equated v3’s marginal compute costs with the overall budget of American labs like OpenAI and Anthropic. This is like saying DeepSeek spent a lot less on apples than OpenAI spent on food. When making an apples-to-apples comparison, DeepSeek spent less, but the difference was far less stark.</li> <li>DeepSeek simultaneously released an app that was free with a remarkably clean design and visible chain-of-thought (CoT). DeepSeek was fast following, so they had no reason to hide the CoT. Comparisons only compared DeepSeek’s top use cases to the same use cases elsewhere, ignoring the features and use cases DeepSeek lacked or did poorly on. So if you wanted to do first-day free querying, you got what was at the time a unique and viral experience. This forced other labs to also show CoT and accelerate release of various models and featu</li></ol>...

When r1 was released in January 2025, there was a DeepSeek moment. When r1-0528 was released in May 2025, there was no moment. Very little talk. Here is a download link for DeepSeek-R1-0528-GGUF. It seems like a solid upgrade. If anything, I wonder if we are underreacting, and this illustrates how hard it is getting to evaluate which models are actually good. What this is not is the proper r2, nor do we have v4. I continue to think that will be a telltale moment. For now, what we have seems to be (but we’re not sure) a model that is solid for its price and status as an open model, but definitely not at the frontier, that you’d use if and only if you wanted to do something that was a very good fit and played to its strong suits. We likely shouldn’t update much either way on v4 and r2, and DeepSeek has a few more months before it starts being conspicuous that we haven’t seen them. WE HAD A MOMENT We all remember The DeepSeek Moment, which led to Panic at the App Store, lots of stock market turmoil that made remarkably little fundamental sense and that has been born out as rather silly, a very intense week and a conclusion to not panic after all. Over several months, a clear picture emerged of (most of) what happened: A confluence of narrative factors transformed DeepSeek’s r1 from an impressive but not terribly surprising model worth updating on into a shot heard round the world, despite the lack of direct ‘fanfare.’ In particular, these all worked together to cause this effect: 1. The ‘six million dollar model’ narrative. People equated v3’s marginal compute costs with the overall budget of American labs like OpenAI and Anthropic. This is like saying DeepSeek spent a lot less on apples than OpenAI spent on food. When making an apples-to-apples comparison, DeepSeek spent less, but the difference was far less stark. 2. DeepSeek simultaneously released an app that was free with a remarkably clean design and visible chain-of-thought (CoT). DeepSeek was fas

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This post is an organizational update from <a href="https://www.aisi.dev/">Georgia Tech’s AI Safety Initiative</a> (AISI) and roughly represents our collective view. In this post, we share lessons & takes from the 2024-25 academic year, describe what we’ve done, and detail our plans for the next academic year. <h1>Introduction</h1>Hey, we’re organizers of <a href="https://www.aisi.dev/">Georgia Tech’s AI Safety Initiative</a> (AISI), thanks for dropping by!  The purpose of this post is to document and share our activities with fellow student AI safety organizations.  Feel free to skip to sections as you need.  We welcome all constructive discussion!  Put any further questions, feedback, or disagreements in the comments and one of us will certainly respond.  A brief outline:<ol><li>Overview and reflection of our activities during the 2024-25 academic year.</li><li>Lessons and strategic takes other AI safety student organizations may benefit from, informed by our activities in the past year.</li><li>Immediate plans, including thoughts on the role of student organizations in the current AI safety landscape.</li></ol>This post is primarily authored by <a href="https://www.linkedin.com/in/yixiong-hao/">Yixiong</a> (co-director) and <a href="https://www.linkedin.com/in/parv-mahajan/">Parv</a> (collaborative initiatives lead).  First and foremost, we’d like to give a HUGE shoutout to our team - <a href="https://www.linkedin.com/in/ayushpanda1029/">Ayush</a>, <a href="https://lesswrong.com/users/neverix">Stepan</a>, <a href="https://www.linkedin.com/in/alec-harris-/">Alec</a>, <a href="https://www.linkedin.com/in/eyas-ayesh-b77743166/">Eyas</a>, <a href="https://www.linkedin.com/in/andrew-j-wei/">Andrew</a>, <a href="https://www.linkedin.com/in/vishnesh/">Vishnesh</a>, <a href="https://www.linkedin.com/in/tanush-chopra/">Tanush</a>, <a href="https://www.linkedin.com/in/harshit-singhal1/">Harshit</a>, and <a href="https://www.linkedin.com/in/jaehunbaek/">Jaehun</a> - for volunteering countless hours despite busy schedules. None of this would’ve been possible without y’all <3.  We would also like to thank Open Philanthropy, our faculty advisors, and external collaborators for supporting our mission.<h1>I. Overview and reflection of our activities</h1>AISI saw significant expansion of our education, outreach, and research activities in the past year; here’s what we’ve been up to:<h2>Intro to AI Safety Fellowship </h2>This is our introductory offering - a technical fellowship distilled from <a href="https://bluedot.org/courses/alignment">BlueDot Impact’s course</a>.  See our syllabus <a href="https://docs.google.com/document/d/1BAw0oX4eyVBXvz_58MeAINmZqonIjHdrsXq9KX1_JFo/edit?usp=sharing">here</a>.  In the past year, we received over 160 applications and hosted 16 cohorts of 6-8 students each.  Cohorts met for ninety minutes every week, and after six weeks had the opportunity to apply for AISI’s research programs and support.  This is an effective program every AIS student organization should have, though it’s high recall and low precision.  We estimate >90% of our current organizers and engaged members are past fel...

This post is an organizational update from Georgia Tech’s AI Safety Initiative (AISI) and roughly represents our collective view. In this post, we share lessons & takes from the 2024-25 academic year, describe what we’ve done, and detail our plans for the next academic year. Introduction Hey, we’re organizers of Georgia Tech’s AI Safety Initiative (AISI), thanks for dropping by! The purpose of this post is to document and share our activities with fellow student AI safety organizations. Feel free to skip to sections as you need. We welcome all constructive discussion! Put any further questions, feedback, or disagreements in the comments and one of us will certainly respond. A brief outline: 1. Overview and reflection of our activities during the 2024-25 academic year. 2. Lessons and strategic takes other AI safety student organizations may benefit from, informed by our activities in the past year. 3. Immediate plans, including thoughts on the role of student organizations in the current AI safety landscape. This post is primarily authored by Yixiong (co-director) and Parv (collaborative initiatives lead). First and foremost, we’d like to give a HUGE shoutout to our team - Ayush, Stepan, Alec, Eyas, Andrew, Vishnesh, Tanush, Harshit, and Jaehun - for volunteering countless hours despite busy schedules. None of this would’ve been possible without y’all <3. We would also like to thank Open Philanthropy, our faculty advisors, and external collaborators for supporting our mission. I. Overview and reflection of our activities AISI saw significant expansion of our education, outreach, and research activities in the past year; here’s what we’ve been up to: Intro to AI Safety Fellowship This is our introductory offering - a technical fellowship distilled from BlueDot Impact’s course. See our syllabus here. In the past year, we received over 160 applications and hosted 16 cohorts of 6-8 students each. Cohorts met for ninety minutes every week, and after

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<h1>Introduction</h1>This is an essay that was grown from a thought during graduate school about how to conceptualise manipulative people and how to handle their behaviour. Initially it was a useful way to help categorise interactions with manipulative people, and from there it grew into strategies to identify holes in their manipulations or protect my own boundaries. Surprisingly the strategies aligned with the advice on handling such malevolent interactions (i.e. grey stoning, firm boundaries, don't play games, record information, limiting interactions, finding support from others).The first section is a treatment of Maxwell's demon and a high level view of the foundation from which I derived this system. While I find it informative and a useful way to frame my thoughts about the demon of interrelation it is not necessary if you understand that:<ol><li>Maxwell's demon is a thought experiment about how something can separate a distribution of hot and cold particles into two boxes</li><li>The demon can use this gradient between two boxes to harvest energy</li><li>This does not violate thermodynamics because information processing necessarily increases the global entropy more than can be harvested from the boxes  </li></ol><h1>Maxwell's Demon and Thermodynamics</h1>For the purposes of this treatment there are several quantities that need to be clearly defined, namely energy, work, entropy, and information. Energy refers to the capacity of a system to do work, which is itself the transfer of energy that occurs when a force moves an object, and colloquially refers to the ability "to do things"; W=∫→F⋅d→r, where W is work, →F is force, and d→r is infinitesimal displacement. Entropy is, on a macroscale, the distribution of energy in a system; while on a microscale entropy is an evaluation of the probability of being in a state given an ensemble of microstates; S=kBlnΩ, where S is entropy, kB is Boltzmann's constant, and Ω is the number of microstates. Information is a way to quantify the amount of surprise that might be had given a particular outcome of some random variable, with Shannon entropy being the expected value of information of a random variable; H(X)=−∑ip(xi)log2p(xi), where H(X) is Shannon entropy, p(xi) is the probability of outcome xi.Maxwell's demon is a creature that sits on the boundary of two box... <style>.mjx-chtml {display: inline-block; line-height: 0; text-indent: 0; text-align: left; text-transform: none; font-style: normal; font-weight: normal; font-size: 100%; font-size-adjust: none; letter-spacing: normal; word-wrap: normal; word-spacing: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0; min-height: 0; border: 0; margin: 0; padding: 1px 0} .MJXc-display {display: block; text-align: center; margin: 1em 0; padding: 0} .mjx-chtml[tabindex]:focus, body :focus .mjx-chtml[tabindex] {display: inline-table} .mjx-full-width {text-align: center; display: table-cell!important; 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Introduction This is an essay that was grown from a thought during graduate school about how to conceptualise manipulative people and how to handle their behaviour. Initially it was a useful way to help categorise interactions with manipulative people, and from there it grew into strategies to identify holes in their manipulations or protect my own boundaries. Surprisingly the strategies aligned with the advice on handling such malevolent interactions (i.e. grey stoning, firm boundaries, don't play games, record information, limiting interactions, finding support from others). The first section is a treatment of Maxwell's demon and a high level view of the foundation from which I derived this system. While I find it informative and a useful way to frame my thoughts about the demon of interrelation it is not necessary if you understand that: 1. Maxwell's demon is a thought experiment about how something can separate a distribution of hot and cold particles into two boxes 2. The demon can use this gradient between two boxes to harvest energy 3. This does not violate thermodynamics because information processing necessarily increases the global entropy more than can be harvested from the boxes Maxwell's Demon and Thermodynamics For the purposes of this treatment there are several quantities that need to be clearly defined, namely energy, work, entropy, and information. Energy refers to the capacity of a system to do work, which is itself the transfer of energy that occurs when a force moves an object, and colloquially refers to the ability "to do things"; W=∫→F⋅d→r, where W is work, →F is force, and d→r is infinitesimal displacement. Entropy is, on a macroscale, the distribution of energy in a system; while on a microscale entropy is an evaluation of the probability of being in a state given an ensemble of microstates; S=kBlnΩ, where S is entropy, kB is Boltzmann's constant, and Ω is the number of microstates. Information is a way to quantify the amount of

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Objective <ul> <li>Translate Alice's voice for Bob to hear in Bob's language. Translate Bob's voice for Alice to hear in Alice's language.</li> <li>Neither person should hear translation of their own voice.</li> <li>Alice and Bob could be in the same room physically or in different rooms.</li> <li>Neither person should hear noise due to closed loop between a mic and speaker.</li> </ul> Zero-code solution <ol> <li>Open: Realtime API in openAI playground in macOS Safari. Input: macOS mic</li> <li>Open: Zoom. Input: Loopback Audio. Output: macOS speaker</li> <li>Open: Rogue Amoeba Loopback.app. Create new device. Safari 1&2 -> Channels 1&2</li> </ol> Do this on only one device for translation one way. Do this on both devices for translation both ways. Once you have this setup working, you can also connect headphones for better noise cancellation if both people are in the same room. Only change required is Zoom Output: Headphones. Prepend each prompt with "translate to French/Chinese/etc" either by speaking these 3 words aloud, or by writing an app that can do it automatically. (I can host this if there's demand.)

Objective * Translate Alice's voice for Bob to hear in Bob's language. Translate Bob's voice for Alice to hear in Alice's language. * Neither person should hear translation of their own voice. * Alice and Bob could be in the same room physically or in different rooms. * Neither person should hear noise due to closed loop between a mic and speaker. Zero-code solution 1. Open: Realtime API in openAI playground in macOS Safari. Input: macOS mic 2. Open: Zoom. Input: Loopback Audio. Output: macOS speaker 3. Open: Rogue Amoeba Loopback.app. Create new device. Safari 1&2 -> Channels 1&2 Do this on only one device for translation one way. Do this on both devices for translation both ways. Once you have this setup working, you can also connect headphones for better noise cancellation if both people are in the same room. Only change required is Zoom Output: Headphones. Prepend each prompt with "translate to French/Chinese/etc" either by speaking these 3 words aloud, or by writing an app that can do it automatically. (I can host this if there's demand.)

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<h2>Introduction</h2>This article explores <a href="https://www.whitehouse.gov/ostp/">White House Office of Science and Technology Policy</a> advisor <a href="https://x.com/deanwball">Dean Ball</a>'s proposal as detailed in his paper "<a href="https://arxiv.org/pdf/2504.11501">A Framework for the Private Governance of Frontier Artificial Intelligence</a>". I think the paper provides a useful insight to how some of the people advising the White House on regulating these issues are thinking about the coming economic transition.Ball's proposal primarily focuses on how to approach the question of civil liability for the customer misuse of models. His proposal outlines a "marketplace" of hybrid public-private standards setting organizations, which must be licensed by the federal<a href="#fn8zdaz0h7ucn">[1]</a> government but are not government agencies themselves. Meeting these standards would not necessarily be mandatory for labs seeking to deploy models, but labs who opted in to meeting at least one such standard would be shielded from tort liability as a result of customer misuse of the models once deployed. In his own words,<blockquote>Private bodies, authorized and overseen by government, provide certifications to developers of frontier AI systems on an opt-in basis. In exchange for opting in, frontier AI firms receive protections from tort liability for customer misuse of their models</blockquote>In one of his <a href="https://substack.com/home/post/p-157928759">Substack</a> posts, Ball did mention <a href="https://www.finra.org/">FINRA</a> as an example of a successful non-governmental standards setting organization. However, FINRA has what is essentially a monopoly within its industry and as such is not a great example of a regulatory marketplace.When I asked him for an example of a successful 'regulatory marketplace' Ball pointed me to the <a href="https://www.londonstockexchange.com/raise-finance/equity/aim">London Stock Exchange's Alternative Investment Market</a>. The LSE's AIM only allows companies to list if they have engaged a "Nominated Advisor" (<a href="https://www.londonstockexchange.com/raise-finance/equity/how-list-equity-listing-journey/role-of-advisers-on-aim">NOMAD</a>), these are private entities who compete for business in much the same way Ball envisions standard setters competing to certify labs. A NOMAD must confirm that the AIM issuer meets admission rules, provide ongoing oversight, and notify the LSE of breaches. While it's unlikely to be a perfect 1/1 comparison, when reading the following proposal you can keep the NOMAD system in mind as a "market comp".With no further ado, let's get into the substance of his proposal.<h2>The Proposed Framework</h2><blockquote>1. A legislature authorizes a government commission to license private AI standards-setting and regulatory organizations. These licenses are granted to organization</blockquote>...

Introduction This article explores White House Office of Science and Technology Policy advisor Dean Ball's proposal as detailed in his paper "A Framework for the Private Governance of Frontier Artificial Intelligence". I think the paper provides a useful insight to how some of the people advising the White House on regulating these issues are thinking about the coming economic transition. Ball's proposal primarily focuses on how to approach the question of civil liability for the customer misuse of models. His proposal outlines a "marketplace" of hybrid public-private standards setting organizations, which must be licensed by the federal[1] government but are not government agencies themselves. Meeting these standards would not necessarily be mandatory for labs seeking to deploy models, but labs who opted in to meeting at least one such standard would be shielded from tort liability as a result of customer misuse of the models once deployed. In his own words, > Private bodies, authorized and overseen by government, provide certifications to developers of frontier AI systems on an opt-in basis. In exchange for opting in, frontier AI firms receive protections from tort liability for customer misuse of their models In one of his Substack posts, Ball did mention FINRA as an example of a successful non-governmental standards setting organization. However, FINRA has what is essentially a monopoly within its industry and as such is not a great example of a regulatory marketplace. When I asked him for an example of a successful 'regulatory marketplace' Ball pointed me to the London Stock Exchange's Alternative Investment Market. The LSE's AIM only allows companies to list if they have engaged a "Nominated Advisor" (NOMAD), these are private entities who compete for business in much the same way Ball envisions standard setters competing to certify labs. A NOMAD must confirm that the AIM issuer meets admission rules, provide ongoing oversight, and notify the LSE of breach

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I investigated this question by having Claude play the advanced social deduction game Blood on the Clocktower. Clocktower is a game similar to Werewolf (or Mafia) where players sit in a circle and are secretly divided into a good team and an evil team. The good players outnumber the evil players, but they don’t know who the evil players are, so they have to share information with each other to deduce who is evil and execute them before it's too late. Meanwhile the evil players try to build trust with the good team and disrupt their deduction as much as possible.Clocktower takes this formula a step further by giving each player a unique character that gives them extra information or lets them disrupt other players’ abilities. Each player is told the set of characters that might be in play for the game and deducing which characters are in play and which are not is an important part of the puzzle.I created <a href="https://james-sullivan.github.io/botc-visualizer/#/game/20250601_012019">this website where you can scroll through an interactive timeline</a> of games and view the players' actions, reasoning, and notes. The website also has a full rules explanation if you are curious.<img src="https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/syYBLEkj5bGswz5yf/lu5x4lhtn359htat3y6q" alt=""><h2 data-internal-id="Agent_Scaffolding">Agent Scaffolding</h2>During the day, each player is given four opportunities to take an action and the order they take actions in is shuffled each cycle so different players get a chance to act first. Players take actions through using Claude’s tool use capability. During the day those actions are message, nominate for execution, pass, or use the Slayer power. Messages can be sent to any number of players and nominations start a vote to execute the chosen player. Night actions are taken using a special night action tool with an interchangeable prompt depending on the choice the player is making.Each player has a history of recent events that gets added to whenever an event they are privy to occurs, such as receiving a message. At the end of each night phase players update their notes file using their history to record the most important events and they are also prompted to write five pieces of actionable strategy advice for how they will help their team win. Their history is then cleared.Each player’s system prompt contains the rules for the game, the list of characters that might be in play, and their history and notes as well as some basic strategy advice.<h1 data-internal-id="My_Observations">My Observations</h1>The following are my observations from reading a few dozen games played using the Claude 3.5 Haiku...

I investigated this question by having Claude play the advanced social deduction game Blood on the Clocktower. Clocktower is a game similar to Werewolf (or Mafia) where players sit in a circle and are secretly divided into a good team and an evil team. The good players outnumber the evil players, but they don’t know who the evil players are, so they have to share information with each other to deduce who is evil and execute them before it's too late. Meanwhile the evil players try to build trust with the good team and disrupt their deduction as much as possible. Clocktower takes this formula a step further by giving each player a unique character that gives them extra information or lets them disrupt other players’ abilities. Each player is told the set of characters that might be in play for the game and deducing which characters are in play and which are not is an important part of the puzzle. I created this website where you can scroll through an interactive timeline of games and view the players' actions, reasoning, and notes. The website also has a full rules explanation if you are curious. Agent Scaffolding During the day, each player is given four opportunities to take an action and the order they take actions in is shuffled each cycle so different players get a chance to act first. Players take actions through using Claude’s tool use capability. During the day those actions are message, nominate for execution, pass, or use the Slayer power. Messages can be sent to any number of players and nominations start a vote to execute the chosen player. Night actions are taken using a special night action tool with an interchangeable prompt depending on the choice the player is making. Each player has a history of recent events that gets added to whenever an event they are privy to occurs, such as receiving a message. At the end of each night phase players update their notes file using their history to record the most important events and they are also prompted

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<figure class="image"><img src="https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/d9XHg67PRDLCDpajJ/m2hkxe8vcsglabwlhctd"><figcaption>Memento (2000)</figcaption></figure>My wife and I are power users of Large Language Models (LLMs). My go-to LLM has been Google Gemini, while she has been extensively using ChatGPT for almost a year. We make an effort to try these models for both personal and professional tasks, making our lives more efficient.As power users of LLMs, we constantly run into their shortcomings. One of the most obvious shortcomings is the models' poor short-term memory. This shortcoming also exists in human beings via a disorder called <a href="https://my.clevelandclinic.org/health/diseases/23221-anterograde-amnesia">anterograde amnesia</a>.What is Anterograde Amnesia?Anterograde amnesia is a type of memory loss that occurs when you can’t form new memories. In the most extreme cases, this means you permanently lose the ability to learn or retain any new information.Temporary anterograde amnesia often occurs with binge drinking. If you’ve ever been blackout drunk, you have experienced anterograde amnesia. Permanent anterograde amnesia is quite rare, often associated with brain damage. It is also very rare as a standalone disorder: typically it occurs alongside retrograde amnesia, forming a disorder we’ve all heard about: dementia.MementoOne of the best representations of standalone permanent anterograde amnesia is the main character of the film <a href="https://www.imdb.com/es/title/tt0209144/">Memento</a>. In the film, Leonard Shelby is forced to navigate the world in a very peculiar way due to his disorder, while trying to avenge the murder of his wife.Leonard has to use several tools such as tattoos, notes, and polaroids to reconstruct memories of previous moments. Very early into the film you begin to realize that Leonard can’t function as a normal human being without assistance. This is one of the most striking similarities between Leonard and LLMs: They both need to be guided and assisted by a human in order to be functional.Things that are simple even to children such as remembering a hotel room or what day it is are extremely difficult for Leonard. Despite having processes to remember, Leonard has to depend on other characters to function both on a day-to-day basis but also working through his long-term goal of finding his wife’s murderer.LLMs with AmnesiaLLMs do not suffer from anterograde amnesia to the degree that Leonard does in Memento. This is because models have context windows, which is the amount of text the model can process and remember at any given time. Models like Gemini 2.5 Pro have context windows of up to 2 million...

Memento (2000) My wife and I are power users of Large Language Models (LLMs). My go-to LLM has been Google Gemini, while she has been extensively using ChatGPT for almost a year. We make an effort to try these models for both personal and professional tasks, making our lives more efficient. As power users of LLMs, we constantly run into their shortcomings. One of the most obvious shortcomings is the models' poor short-term memory. This shortcoming also exists in human beings via a disorder called anterograde amnesia. What is Anterograde Amnesia? Anterograde amnesia is a type of memory loss that occurs when you can’t form new memories. In the most extreme cases, this means you permanently lose the ability to learn or retain any new information. Temporary anterograde amnesia often occurs with binge drinking. If you’ve ever been blackout drunk, you have experienced anterograde amnesia. Permanent anterograde amnesia is quite rare, often associated with brain damage. It is also very rare as a standalone disorder: typically it occurs alongside retrograde amnesia, forming a disorder we’ve all heard about: dementia. Memento One of the best representations of standalone permanent anterograde amnesia is the main character of the film Memento. In the film, Leonard Shelby is forced to navigate the world in a very peculiar way due to his disorder, while trying to avenge the murder of his wife. Leonard has to use several tools such as tattoos, notes, and polaroids to reconstruct memories of previous moments. Very early into the film you begin to realize that Leonard can’t function as a normal human being without assistance. This is one of the most striking similarities between Leonard and LLMs: They both need to be guided and assisted by a human in order to be functional. Things that are simple even to children such as remembering a hotel room or what day it is are extremely difficult for Leonard. Despite having processes to remember, Leonard has to depend on other charac

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We know what linear functions are. A function f is linear iff it satisfies additivity f(x+y)=f(x)+f(y) and homogeneity f(ax)=af(x).We know what continuity is. A function f is continuous iff for all ε there exists a δ such that if |x−x0| < δ, then |f(x)−f(x0)| < ε.An equivalent way to think about continuity is the sequence criterion: f is continuous iff a sequence (xk) converging to x implies that (f(xk)) converges to f(x). That is to say, if for all ε there exists an N such that if k ≥ N, then |xk−x| < ε, then for all ε, there also exists an M such that if k ≥ M, then |f(xk)−f(x)| < ε.Sometimes people talk about discontinuous linear functions. You might think: that's crazy. I've seen many linear functions in my time, and they were definitely all continuous. f(x): ℝ → ℝ := ax is continuous for any a ∈ ℝ. T(x⃗): ℝ² → ℝ² := (abcd)→x is continuous no matter what the entries in the matrix are. Stop being crazy!!Actually, it's not crazy. It's just that all the discontinuous linear functions live in infinite-dimensional spaces.Take, say, the space C1([a,b]) of continuously differentiable functions from a closed interval [a,b] to ℝ, with the uniform norm. (The uniform norm means that the "size" of a function for the purposes of continuity is the least upper bound of its absolute value.) If you think of a vector in the n-dimensional Rn as a function from {1...n} to ℝ, then you can see why a function from a continuous (not even countable) domain would be infinite-dimensional.Consider the sequence of functions (fk)=(sinkxk)∞k=1 in C1([a,b]). The sequence converges to the zero function: for any ε, we can take N:=⌈1ε⌉ and then sinkxk≤1⌈1ε⌉≤11ε=ε.Now consider that the sequence of derivatives is (kcoskxk)∞k=1=(coskx)∞k=1, which doesn't converge. But the function D: $C^1([a,b]) \rightarrow C0([a,b]) that maps a function to its derivative is linear. (We have additivity because the derivative of a sum is the sum of the derivatives, and we have homogeneity because you can "pull out" a constant factor from the derivative.)By exhibiting a function D and a sequence (fk) for which (fk) converges but (D(fk)) doesn't, we have shown that the derivative mapping D is a discontinuous linear function, because the sequence criterion for continuity is not satisfied. 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We know what linear functions are. A function f is linear iff it satisfies additivity f(x+y)=f(x)+f(y) and homogeneity f(ax)=af(x). We know what continuity is. A function f is continuous iff for all ε there exists a δ such that if |x−x0| < δ, then |f(x)−f(x0)| < ε. An equivalent way to think about continuity is the sequence criterion: f is continuous iff a sequence (xk) converging to x implies that (f(xk)) converges to f(x). That is to say, if for all ε there exists an N such that if k ≥ N, then |xk−x| < ε, then for all ε, there also exists an M such that if k ≥ M, then |f(xk)−f(x)| < ε. Sometimes people talk about discontinuous linear functions. You might think: that's crazy. I've seen many linear functions in my time, and they were definitely all continuous. f(x): ℝ → ℝ := ax is continuous for any a ∈ ℝ. T(x⃗): ℝ² → ℝ² := (abcd)→x is continuous no matter what the entries in the matrix are. Stop being crazy!! Actually, it's not crazy. It's just that all the discontinuous linear functions live in infinite-dimensional spaces. Take, say, the space C1([a,b]) of continuously differentiable functions from a closed interval [a,b] to ℝ, with the uniform norm. (The uniform norm means that the "size" of a function for the purposes of continuity is the least upper bound of its absolute value.) If you think of a vector in the n-dimensional Rn as a function from {1...n} to ℝ, then you can see why a function from a continuous (not even countable) domain would be infinite-dimensional. Consider the sequence of functions (fk)=(sinkxk)∞k=1 in C1([a,b]). The sequence converges to the zero function: for any ε, we can take N:=⌈1ε⌉ and then sinkxk≤1⌈1ε⌉≤11ε=ε. Now consider that the sequence of derivatives is (kcoskxk)∞k=1=(coskx)∞k=1, which doesn't converge. But the function D: $C^1([a,b]) \rightarrow C0([a,b]) that maps a function to its derivative is linear. (We have additivity because the derivative of a sum is the sum of the derivatives, and we have homogeneity because you ca

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Large language models (LLMs) are often fine-tuned after training using methods like reinforcement learning from human feedback (RLHF). In this process, models are rewarded for generating responses that people rate highly. But what people like isn’t always what’s true. Studies have found that models <a href="https://arxiv.org/abs/2310.13548">learn to give answers that humans prefer but are untrue</a>. This problem occurred in a <a href="https://openai.com/index/expanding-on-sycophancy/">recent update to the GPT-4o model</a> that aimed to please the user even by making false statements.Today, we have high-accuracy "lie-detectors” that analyze internal model states—AI's "thought patterns"—to identify deceptive outputs that human reviewers could easily overlook. Even simple logistic models trained on these internal activations can <a href="https://arxiv.org/abs/2502.03407">successfully pinpoint 95-99% of deceptive responses</a>.However, lie detectors are not infallible either. We wanted to find out if adding a lie detector to the training loop would make models honest, or if it would just train models to evade detection. It turns out that models become honest under the right conditions—high detector true positive rate, high KL regularization to an honest original model, and off-policy post-training methods.<h2>Using lie detectors for scalable oversight</h2><figure class="image image_resized" style="width:543.195px"><img src="https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/dT7mvHzuX46vydt9K/ixh9gkr6wlcws2wbhpei" alt=""></figure>Given that models are incentivized to be deceptive, and appear to ‘know’ that they are being deceptive, we could simply train a lie detector with a small number of known truthful/deceptive examples, and use this to assist labellers who cannot easily identify model deception. We call this approach Scalable Oversight via Lie Detector, or "SOLiD”.This approach has the downside that it doesn't remove the incentive to lie: it's still better for the model to tell a lie that humans prefer, as long as it can ‘fool’ the lie detector. Instead of training models to tell the truth, we could end up training models to be better at cheating lie detectors.We wanted to find out if models would learn to tell the truth, or simply become 'better liars’. If models always learn to tell the truth, this could be a very useful tool for AI alignment. But if models instead learn to cheat, training with lie detectors could backfire by making future detectors ineffective.<h2>Our setup </h2>To find out, we modeled a scenario where model developers deploy lie detectors to flag deceptive outputs to human labelers. We assume human labelers prefer deceptive responses if they are unaware of the deception, but st...

Large language models (LLMs) are often fine-tuned after training using methods like reinforcement learning from human feedback (RLHF). In this process, models are rewarded for generating responses that people rate highly. But what people like isn’t always what’s true. Studies have found that models learn to give answers that humans prefer but are untrue. This problem occurred in a recent update to the GPT-4o model that aimed to please the user even by making false statements. Today, we have high-accuracy "lie-detectors” that analyze internal model states—AI's "thought patterns"—to identify deceptive outputs that human reviewers could easily overlook. Even simple logistic models trained on these internal activations can successfully pinpoint 95-99% of deceptive responses. However, lie detectors are not infallible either. We wanted to find out if adding a lie detector to the training loop would make models honest, or if it would just train models to evade detection. It turns out that models become honest under the right conditions—high detector true positive rate, high KL regularization to an honest original model, and off-policy post-training methods. Using lie detectors for scalable oversight Given that models are incentivized to be deceptive, and appear to ‘know’ that they are being deceptive, we could simply train a lie detector with a small number of known truthful/deceptive examples, and use this to assist labellers who cannot easily identify model deception. We call this approach Scalable Oversight via Lie Detector, or "SOLiD”. This approach has the downside that it doesn't remove the incentive to lie: it's still better for the model to tell a lie that humans prefer, as long as it can ‘fool’ the lie detector. Instead of training models to tell the truth, we could end up training models to be better at cheating lie detectors. We wanted to find out if models would learn to tell the truth, or simply become 'better liars’. If models always learn to tell the t

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This is a linkpost for [<a href="https://www.meridiancambridge.org/language-models-course">https://www.meridiancambridge.org/language-models-course</a>] Meridian Cambridge, in partnership with Cambridge University's <a href="https://www.c2d3.cam.ac.uk/">Center for Data Driven Discovery</a> (C2D3),  has produced a 16-part lecture series entitled "Language Models and Intelligent Agentic Systems" and the recordings are now online! The LMaIAS course provides an introduction to core ideas in AI safety. Throughout, we build up from introductory ideas about language modelling and neural networks to discussions of risks posed by advanced AI systems.<h2>Course Structure</h2>The course is divided into four parts:<h3>Part 1: What is a Language Model?</h3>To start the course, we give three lectures covering generative models and next token prediction, the transformer architecture, and scaling laws for large models.<h3>Part 2: Crafting Agentic Systems</h3>Now the foundations are in place, the next four lectures go into details on LLM post-training, reinforcement learning, reward modelling, and agent architectures.<h3>Part 3: Agentic Behaviour</h3>Here we take four lectures to discuss optimisation and reasoning, reward hacking and goal misgeneralisation, out-of-context reasoning and situational awareness, and finally deceptive alignment and alignment faking.<h3>Part 4: Frontiers</h3>For the remainder of the lecture series, we give five lectures covering risks from advanced AI, AI evaluations, AI control and safety cases, AI organisations and agendas, and conclude with a discussion on the future of language models.You can find the first lecture [<a href="https://www.youtube.com/watch?v=UiGa8Bx1Srk&list=PLKOGQ4KczC_pa2r4UXb3wOIbBeU6Qeeoy&index=1">here</a>], and the whole course is available [<a href="https://www.meridiancambridge.org/language-models-course">here</a>]. The lectures were created and delivered by Edward James Young, Jason R. Brown, and Lennie Wells, in partnership with Cambridge University's C2D3. The hope is that this material can be used to help educate people new to the field and provide them with the background knowledge required to effectively contribute to AI safety. Please share this with anybody you think might be interested!- The Meridian Team

This is a linkpost for [https://www.meridiancambridge.org/language-models-course] Meridian Cambridge, in partnership with Cambridge University's Center for Data Driven Discovery (C2D3), has produced a 16-part lecture series entitled "Language Models and Intelligent Agentic Systems" and the recordings are now online! The LMaIAS course provides an introduction to core ideas in AI safety. Throughout, we build up from introductory ideas about language modelling and neural networks to discussions of risks posed by advanced AI systems. Course Structure The course is divided into four parts: Part 1: What is a Language Model? To start the course, we give three lectures covering generative models and next token prediction, the transformer architecture, and scaling laws for large models. Part 2: Crafting Agentic Systems Now the foundations are in place, the next four lectures go into details on LLM post-training, reinforcement learning, reward modelling, and agent architectures. Part 3: Agentic Behaviour Here we take four lectures to discuss optimisation and reasoning, reward hacking and goal misgeneralisation, out-of-context reasoning and situational awareness, and finally deceptive alignment and alignment faking. Part 4: Frontiers For the remainder of the lecture series, we give five lectures covering risks from advanced AI, AI evaluations, AI control and safety cases, AI organisations and agendas, and conclude with a discussion on the future of language models. You can find the first lecture [here], and the whole course is available [here]. The lectures were created and delivered by Edward James Young, Jason R. Brown, and Lennie Wells, in partnership with Cambridge University's C2D3. The hope is that this material can be used to help educate people new to the field and provide them with the background knowledge required to effectively contribute to AI safety. Please share this with anybody you think might be interested! - The Meridian Team

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<h2>sodium electrolysis</h2> Aluminum metal is a widely-used material. It costs ~$2.5/kg. A significant fraction of its production cost is electricity.Currently, Na metal is produced by <a href="https://en.wikipedia.org/wiki/Downs_cell">Downs cell</a> electrolysis of NaCl. Making sodium metal with electrolysis requires much less energy per mass of sodium than making aluminum. The raw material used (NaCl) is very cheap. Why, then, is sodium several times as expensive as aluminum? There's not a clear market price for it, but it's typically considered to be ~$10/kg on a large scale.Partly, that's because: <ul> <li>The scale of production of Na is much smaller.</li> <li>Transport is more expensive.</li> </ul> But the process is also inherently more expensive. Why?The cells used for Al electrolysis are open to the atmosphere. Oxygen and CO2 comes out of them. Electrolysis of NaCl produces Cl2, which is too hazardous to just release. So, it has to be collected. NaCl has a boiling point of 1413 C, so some salt evaporates, which causes problems in the chlorine handling system.Na metal has a relatively high solubility in NaCl, so it continuously reacts with the generated chlorine, reducing efficiency. <h2>a new process</h2> There's a <a href="https://www.sciencedirect.com/science/article/pii/S2213956724000616">recent paper</a> (open access) describing a new method for producing Na metal. The idea is: <ul> <li> Add Na carbonate so that electrolysis produces O2 instead of Cl2. </li> <li> Normally, adding carbonate leads to carbon buildup on the electrode, because the voltage for that is basically the same as Na production. So, they use a liquid tin electrode, which reduces the voltage for Na electrolysis. </li> <li> That of course means that some energy is needed to separate the Na from the tin. They do that separation with vacuum distillation. This isn't unprecedented for metal production: most Mg metal production uses the <a href="https://en.wikipedia.org/wiki/Pidgeon_process">Pidgeon process</a> which involves separating Mg metal by distillation. In this case, the effective boiling point of the Na might be increased by ~400 K. </li> </ul> The professor involved in that paper also previously did <a href="https://link.springer.com/article/10.1007/s11663-023-02945-8">a similar thing</a> with potassium.Of course, most Na carbonate is made from NaCl, and the chlorine has to go somewhere. Combining the above electrolysis with the <a href="https://en.wikipedia.org/wiki/Solvay_process">Solvay Process</a>, the net reaction would be:2 NaCl + CaCO3 -> 2 Na + CaCl2 + CO2 + 1/2 O2How do I find things like this? Well, in this case, I noticed that paper because I searched Google Scholar for papers doing this exact thing because I was curious if anyone tried it. <h2>cost estimation</h2> <h3>sodi</h3>...

sodium electrolysis Aluminum metal is a widely-used material. It costs ~$2.5/kg. A significant fraction of its production cost is electricity. Currently, Na metal is produced by Downs cell electrolysis of NaCl. Making sodium metal with electrolysis requires much less energy per mass of sodium than making aluminum. The raw material used (NaCl) is very cheap. Why, then, is sodium several times as expensive as aluminum? There's not a clear market price for it, but it's typically considered to be ~$10/kg on a large scale. Partly, that's because: * The scale of production of Na is much smaller. * Transport is more expensive. But the process is also inherently more expensive. Why? The cells used for Al electrolysis are open to the atmosphere. Oxygen and CO2 comes out of them. Electrolysis of NaCl produces Cl2, which is too hazardous to just release. So, it has to be collected. NaCl has a boiling point of 1413 C, so some salt evaporates, which causes problems in the chlorine handling system. Na metal has a relatively high solubility in NaCl, so it continuously reacts with the generated chlorine, reducing efficiency. a new process There's a recent paper (open access) describing a new method for producing Na metal. The idea is: * Add Na carbonate so that electrolysis produces O2 instead of Cl2. * Normally, adding carbonate leads to carbon buildup on the electrode, because the voltage for that is basically the same as Na production. So, they use a liquid tin electrode, which reduces the voltage for Na electrolysis. * That of course means that some energy is needed to separate the Na from the tin. They do that separation with vacuum distillation. This isn't unprecedented for metal production: most Mg metal production uses the Pidgeon process which involves separating Mg metal by distillation. In this case, the effective boiling point of the Na might be increased by ~400 K. The professor involved in that paper also previously did a similar thing with potassium.

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As you know, histograms are decent visualizations for PDFs with lots of samples...<figure class="image"><img src="https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/LFGgwitjertJqch7J/okziqklkhzdtnykkgv1u"><figcaption>10k predictions, 20 bins</figcaption></figure> ...but if there are only a few samples, the histogram-binning choices can matter a lot:<figure class="image image_resized" style="width:67.35%"><img src="https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/LFGgwitjertJqch7J/t7g2wvgunxnd3bjdsrty"><figcaption>10 predictions, 4 bins</figcaption></figure><figure class="image image_resized" style="width:67.72%"><img src="https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/LFGgwitjertJqch7J/jylvb6vl47cnqgampvoc"><figcaption>same 10 predictions, 7 bins</figcaption></figure>The binning (a) discards information, and worse, (b) is mathematically un-aesthetic.But a CDF doesn't have this problem!<figure class="image image_resized" style="width:76.39%"><img src="https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/LFGgwitjertJqch7J/km9mrozopdjo9ck6l7nf"><figcaption>same 10 predictions, every data point precisely represented</figcaption></figure><hr>If you make a bunch of predictions, and you want to know how well they're calibrated, classically you make a graph like this:<figure class="image image_resized" style="width:55.16%"><img src="https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/LFGgwitjertJqch7J/ljw1evuczrszoilmako3"><figcaption>source: <a href="https://slatestarcodex.com/blog_images/calibration2019.png">SSC's 2019 prediction grading</a></figcaption></figure>But, as with a histogram, this depends on how you bin your predictions.<figure class="image image_resized" style="width:65.87%"><img src="https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/LFGgwitjertJqch7J/zsualkqmgrqtsnflfwwv"><figcaption>100 predictions, 10 bins</figcaption></figure><figure class="image image_resized" style="width:67.31%"><img src="https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/LFGgwitjertJqch7J/oqwrv6fcdvts1jmmqdca"><figcaption>same 100 predictions, 30 bins</figcaption></figure>Is there some CDF-like equivalent here? Some visualization with no free parameters?<hr>I asked that question to several people at Arbor Summer Camp. I got three answers:<ol><li>"You get from a PDF to a CDF by integrating. So, here, analogously, let's integrate (num predictions with confidence < x that came true) minus (expected num predictions with confidence < x that came true)."</li><li>(the same thing, said in different words)</li><li>(the same thing, said in different words)</li></ol>If we make a "CDF" for the above 100 predictions by applying these three insights, we get:<figure class="image image_resized" style="width:78.49%"><img src="https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/LFGgwitjertJqch7J/apxdm8qtpdgjuztijb0j" alt="CDF for calibration curves"><figcaption><a href="https://optimizationprocess.com/calibration-cdf/assets/calcdf.py">.py</a></figcaption></figure>I find this a little harder to read than the calibration plots above, which I choose to interpret as a good sign, since CDFs are a little harder to read than histograms. The thing to keep in mind, I think, is: when the curve is going up, it's a sign your probabilities are too high; when it's going down, it's a sign your probabilities are too low.<blockquote>Test: how would you describe the problems that this predictor has?<figure class="image image_resized" style="width:86.21%"><img src="https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/LFGgwitjertJqch7J/ytfjykphmxl5kuxyvf48"></figure><a href="https://itty.bitty.site/#/?eJxtkE1ug0AMha/ylE1aCaX7CNFFolwg6gEGxoCFY5PxUJTbV1AW6c/Ov+97djlWHxopNaYtR9J8RGsJfgsiGJPVoWbhzOQFck/Ync6XHdiR2Fm7Am5r/ccoOO8dwZ07Ze0QEqEjpRREHshm6LnrC8w9C628Jf8PdzpfFlgbRJ5p39IUf2H/csTmQ/k2VuVYlXWqtvBKjWnEfSLPbHrE3IeM2SaJCAr7fPoHxkSRm2xp79v1YjZAeKD3Te/lSrT6Io2wFrcHhHVY/Jnn9cCl6ybTwju8Lntf+bKFyQ==">Solution.</a></blockquote> (Are there any better visualizations? Maybe. I <a href="https://www.lesswrong.com/posts/sa8Qhby63QLCzcCDo/is-there-an-equivalent-of-the-cdf-for-grading-predictions?commentId=eYvAa3xfx2BpAcKDn">looked into this a couple years ago</a>, but looking back at it, I think this simple "sum(expected-actual predictions with p<x)" graph is at least as compelling as anything I found.)

As you know, histograms are decent visualizations for PDFs with lots of samples... 10k predictions, 20 bins ...but if there are only a few samples, the histogram-binning choices can matter a lot: 10 predictions, 4 binssame 10 predictions, 7 bins The binning (a) discards information, and worse, (b) is mathematically un-aesthetic. But a CDF doesn't have this problem! same 10 predictions, every data point precisely represented ---------------------------------------- If you make a bunch of predictions, and you want to know how well they're calibrated, classically you make a graph like this: source: SSC's 2019 prediction grading But, as with a histogram, this depends on how you bin your predictions. 100 predictions, 10 binssame 100 predictions, 30 bins Is there some CDF-like equivalent here? Some visualization with no free parameters? ---------------------------------------- I asked that question to several people at Arbor Summer Camp. I got three answers: 1. "You get from a PDF to a CDF by integrating. So, here, analogously, let's integrate (num predictions with confidence < x that came true) minus (expected num predictions with confidence < x that came true)." 2. (the same thing, said in different words) 3. (the same thing, said in different words) If we make a "CDF" for the above 100 predictions by applying these three insights, we get: .py I find this a little harder to read than the calibration plots above, which I choose to interpret as a good sign, since CDFs are a little harder to read than histograms. The thing to keep in mind, I think, is: when the curve is going up, it's a sign your probabilities are too high; when it's going down, it's a sign your probabilities are too low. > Test: how would you describe the problems that this predictor has? > > Solution. (Are there any better visualizations? Maybe. I looked into this a couple years ago, but looking back at it, I think this simple "sum(expected-actual predictions with p<x)" graph is

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I put together a model explaining the link between IQ and the onset of puberty. Ideas only thrive when they're attacked, and I was told this was the kind of place that could manage it. The core idea is that slowing myelination in the brain extends its plasticity window and encourages more robust and efficient thalamo-cortical integration. By considering latency, the model naturally gives rise to Deco and Kringelbach's Turbulence model by turning cognition into a latency/energy optimization problem. From what I can tell, it fits a surprising amount of cross-disciplinary evidence, but I need counter-examples more than anything.

I put together a model explaining the link between IQ and the onset of puberty. Ideas only thrive when they're attacked, and I was told this was the kind of place that could manage it. The core idea is that slowing myelination in the brain extends its plasticity window and encourages more robust and efficient thalamo-cortical integration. By considering latency, the model naturally gives rise to Deco and Kringelbach's Turbulence model by turning cognition into a latency/energy optimization problem. From what I can tell, it fits a surprising amount of cross-disciplinary evidence, but I need counter-examples more than anything.

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Disempowerment is on the fence, gets interpreted as either implying human extinction or being a good place. "Doom" tends to be ambiguous between disempowerment and extinction, as well as about when that outcome must be gauged. And many people currently feel both disempowered and OK, so see eutopia as similar to disempowerment, neither an example of "doom".Arguments pointing to risk of human extinction run into the issue of people expecting disempowerment without extinction, when some of the same arguments would remain relevant if applied directly to disempowerment (including the moral arguments about extinction or disempowerment being a problem). And arguments pointing to desirability of establishing eutopia run into the issue of people expecting disempowerment to be approximately as good and in practice much more likely. When the distinctions between these levels of doom are not maintained, conflation makes it harder to meaningfully disagree. <h2>Eutopia Without Disempowerment</h2> This distinction might be slightly more murky, worth defining more explicitly. For me, a crux of a future that's good for humanity is giving the biological humans the resources and the freedom to become transhuman beings themselves, with no hard ceiling on relevance in the long run. Rather than AIs only letting some originally-humans to grow into more powerful but still purely ornamental roles, or not letting them grow at all, or not letting them think faster and do checkpointing and multiple instantiations of the mind states using a non-biological cognitive substrate, or letting them unwillingly die of old age or disease. This should only apply to those who so choose, under their own direction rather than only through externally imposed uplifting protocols, even if that leaves it no more straightforward than world-class success of some kind today, to reach a sensible outcome.This in particular implies reasonable resources being left to those who remain/become regular biological humans (or take their time growing up), including through influence of some of these originally-human beings who happen to consider that a good thing to ensure. <h2>Yudkowsky's Arguments and Disempowerment</h2> Yudkowsky frames <a href="https://www.lesswrong.com/posts/uMQ3cqWDPHhjtiesc/agi-ruin-a-list-of-lethalities">AGI ruin arguments</a> around extinction, which his models predict. I think many of the same arguments survive in a world where some AIs have a <a href="https://www.lesswrong.com/posts/2NncxDQ3KBDCxiJiP/cosmopolitan-values-don-t-come-free?commentId=ofPTrG6wsq7CxuTXk">minimal level of caring about humanity</a> <a href="https://www.lesswrong.com/posts/xvBZPEccSfM8Fsobt/what-are-the-best-arguments-for-against-ais-being-slightly">sufficient to prese</a>...

Disempowerment is on the fence, gets interpreted as either implying human extinction or being a good place. "Doom" tends to be ambiguous between disempowerment and extinction, as well as about when that outcome must be gauged. And many people currently feel both disempowered and OK, so see eutopia as similar to disempowerment, neither an example of "doom". Arguments pointing to risk of human extinction run into the issue of people expecting disempowerment without extinction, when some of the same arguments would remain relevant if applied directly to disempowerment (including the moral arguments about extinction or disempowerment being a problem). And arguments pointing to desirability of establishing eutopia run into the issue of people expecting disempowerment to be approximately as good and in practice much more likely. When the distinctions between these levels of doom are not maintained, conflation makes it harder to meaningfully disagree. Eutopia Without Disempowerment This distinction might be slightly more murky, worth defining more explicitly. For me, a crux of a future that's good for humanity is giving the biological humans the resources and the freedom to become transhuman beings themselves, with no hard ceiling on relevance in the long run. Rather than AIs only letting some originally-humans to grow into more powerful but still purely ornamental roles, or not letting them grow at all, or not letting them think faster and do checkpointing and multiple instantiations of the mind states using a non-biological cognitive substrate, or letting them unwillingly die of old age or disease. This should only apply to those who so choose, under their own direction rather than only through externally imposed uplifting protocols, even if that leaves it no more straightforward than world-class success of some kind today, to reach a sensible outcome. This in particular implies reasonable resources being left to those who remain/become regular biological humans (or

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I am a PhD student in computer science at the University of Waterloo, supervised by Professor Ming Li and advised by Professor Marcus Hutter.My current research is related to applications of algorithmic probability to sequential decision theory (universal artificial intelligence). Recently I have been trying to start a dialogue between the computational cognitive science and UAI communities. Sometimes I build robots, professionally or otherwise. Another hobby (and a personal favorite of my posts here) is the <a href="https://www.lesswrong.com/posts/Yz33koDN5uhSEaB6c/sherlockian-abduction-master-list">Sherlockian abduction master list,</a> which is a crowdsourced project seeking to make "Sherlock Holmes" style inference feasible by compiling observational cues. Give it a read and see if you can contribute!See my personal website <a href="https://colewyeth.com/">colewyeth.com</a> for an overview of my interests and work.I do ~two types of writing, academic publications and (lesswrong) posts. With the former I try to be careful enough that I can stand by ~all (strong/central) claims in 10 years, usually by presenting a combination of theorems with rigorous proofs and only more conservative intuitive speculation. With the later, I try to learn enough by writing that I have changed my mind by the time I'm finished - and though I usually include an "epistemic status" to suggest my (final) degree of confidence before posting, the ensuing discussion often changes my mind again.

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Epistemic status: One week empirical project from a theoretical computer scientist. My analysis and presentation were both a little rushed; some information that would be interesting is missing from plots because I simply did not have time to include it. All known "breaking" issues are discussed and should not effect the conclusions. I may refine this post in the future.[This work was performed as my final project for ARENA 5.0.]<h2>Background</h2>I have seen several claims<a href="#fn1dkbg31nfrm">[1]</a> in the literature that base LLM in-context learning (ICL) can be understood as approximating Solomonoff induction. <a href="https://www.lesswrong.com/posts/vvgND6aLjuDR6QzDF/my-model-of-what-is-going-on-with-llms">I lean on this intuition a bit myself </a>(and I am in fact a co-author of one of those papers). However, I have not seen any convincing empirical evidence for this model. From a theoretical standpoint, it is a somewhat appealing idea. LLMs and Solomonoff induction both face the so-called "prequential problem," predicting a sequence based on a prefix seen so far with a loss function that incentivizes calibration (the log loss; an LLM's loss function may also include other regularization terms like weight decay). Also, ICL is more sample efficient than pretraining. For me, this dovetails with Shane Legg's argument<a href="#fnjoen89isydk">[2]</a> that there is no elegant universal theory of prediction, because an online predictor must be complex to learn complex sequences successfully. LLM pretraining is a pretty simple algorithm, but LLM ICL is a very complicated algorithm which leverages a massive number of learned parameters. This is an incomplete argument; Solomonoff induction is a highly general sample efficient algorithm for the prequential problem, as is LLM ICL, but that does not mean they are meaningfully connected. In fact, they are optimized for different distributions: the universal distribution versus the distribution of text on the internet. Arguably, the later may be a special case of the former with an appropriate choice of universal Turing machine (UTM), but I find this perspective to be a bit of a stretch. At the very least I expect LLM ICL to be similar to a universal distribution conditioned on some background information.In order to gather some empirical evidence, I adapted this <a href="https://github.com/google-deepmind/neural_networks_solomonoff_induction">approximate universal distribution sampler</a> from Google DeepMind. They used the samples to train a transformer to directly approximate Solomonoff induction in-context, which I will call the Solomonoff Induc...

Epistemic status: One week empirical project from a theoretical computer scientist. My analysis and presentation were both a little rushed; some information that would be interesting is missing from plots because I simply did not have time to include it. All known "breaking" issues are discussed and should not effect the conclusions. I may refine this post in the future. [This work was performed as my final project for ARENA 5.0.] Background I have seen several claims[1] in the literature that base LLM in-context learning (ICL) can be understood as approximating Solomonoff induction. I lean on this intuition a bit myself (and I am in fact a co-author of one of those papers). However, I have not seen any convincing empirical evidence for this model. From a theoretical standpoint, it is a somewhat appealing idea. LLMs and Solomonoff induction both face the so-called "prequential problem," predicting a sequence based on a prefix seen so far with a loss function that incentivizes calibration (the log loss; an LLM's loss function may also include other regularization terms like weight decay). Also, ICL is more sample efficient than pretraining. For me, this dovetails with Shane Legg's argument[2] that there is no elegant universal theory of prediction, because an online predictor must be complex to learn complex sequences successfully. LLM pretraining is a pretty simple algorithm, but LLM ICL is a very complicated algorithm which leverages a massive number of learned parameters. This is an incomplete argument; Solomonoff induction is a highly general sample efficient algorithm for the prequential problem, as is LLM ICL, but that does not mean they are meaningfully connected. In fact, they are optimized for different distributions: the universal distribution versus the distribution of text on the internet. Arguably, the later may be a special case of the former with an appropriate choice of universal Turing machine (UTM), but I find this perspective to be a bit of a

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Fundamental Uncertainty: Chapter 2 - How do words get their meaning?

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I'm writing a <a href="https://www.fundamentaluncertainty.com/">book</a> about epistemology. It's about <a href="https://www.lesswrong.com/posts/Xs7ag4gsiA6zspmsD/the-problem-of-the-criterion">The Problem of the Criterion</a>, why it's important, and what it has to tell us about how we approach knowing the truth.I've also written a lot about AI safety. Some of the more interesting stuff can be found at the site of my currently-dormant AI safety org, <a href="https://paisri.org/">PAISRI</a>.

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N.B. This is a chapter in a book about truth and knowledge. It's a major revision to the version of <a href="https://www.lesswrong.com/posts/Kq9nq5hvPHanrmdNZ/fundamental-uncertainty-chapter-2-why-do-words-have-meaning">this chapter</a> in the first draft, so I'm publishing it again to get feedback on the completely new text, and have replaced the original version of the chapter with this one in the sequence. You can find more info about the book on its <a href="https://www.fundamentaluncertainty.com/">website</a>.Like many people, I have a hard time learning foreign languages. I've tried dozens of apps, books, and courses to learn everything from Mandarin Chinese to Classical Greek, all with little success. The closest I ever came to really learning another language was when I took French in high school, but I was so far from fluency that, after three years of intense study, I was lucky to receive a passing grade.I started out with high hopes that learning French would be easy. All I had to do, it seemed, was memorize the mapping of French words to English ones. If I could remember that "le cheval" means horse and "la voiture" means car and so on, I'd be able to speak French fluently.Unfortunately, it didn't work. I couldn't map between French and English fast enough to do anything more than read and write French very slowly. My teacher said I needed to learn to think in French and stop trying to translate everything in my head, but I didn't want to believe him. I was convinced that my way should work. What finally changed my mind was discovering that some English words lack a French equivalent.Consider the word "mug". In English it encompasses many different types of cups made of different materials, from glass beer mugs to clay coffee mugs to insulated metal travel mugs and more. But in French a beer mug is "une chope", a coffee mug is "une tasse à café", and a travel mug might be "un thermos" or "un gobelet de voyage". Each one gets its own name, and there is no one word like "mug" that groups them all together. The closest translations of "mug" are the loanword "le mug"—which usually means a coffee mug—and the descriptive phrase "les tasses à anse"—cups with handles—but neither option succeeds in capturing all the implicit meaning carried by "mug" in English. There's simply no word in French that means exactly what "mug" does.And "mug" is just one example. English has hundreds of words like "cozy" and "snack" that lack native equivalents in French, and thousands more where the meaning of the straightforward translation is close...

N.B. This is a chapter in a book about truth and knowledge. It's a major revision to the version of this chapter in the first draft, so I'm publishing it again to get feedback on the completely new text, and have replaced the original version of the chapter with this one in the sequence. You can find more info about the book on its website. Like many people, I have a hard time learning foreign languages. I've tried dozens of apps, books, and courses to learn everything from Mandarin Chinese to Classical Greek, all with little success. The closest I ever came to really learning another language was when I took French in high school, but I was so far from fluency that, after three years of intense study, I was lucky to receive a passing grade. I started out with high hopes that learning French would be easy. All I had to do, it seemed, was memorize the mapping of French words to English ones. If I could remember that "le cheval" means horse and "la voiture" means car and so on, I'd be able to speak French fluently. Unfortunately, it didn't work. I couldn't map between French and English fast enough to do anything more than read and write French very slowly. My teacher said I needed to learn to think in French and stop trying to translate everything in my head, but I didn't want to believe him. I was convinced that my way should work. What finally changed my mind was discovering that some English words lack a French equivalent. Consider the word "mug". In English it encompasses many different types of cups made of different materials, from glass beer mugs to clay coffee mugs to insulated metal travel mugs and more. But in French a beer mug is "une chope", a coffee mug is "une tasse à café", and a travel mug might be "un thermos" or "un gobelet de voyage". Each one gets its own name, and there is no one word like "mug" that groups them all together. The closest translations of "mug" are the loanword "le mug"—which usually means a coffee mug—and the descriptive phras

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(Crosspost from <a href="https://benthams.substack.com/p/ai-might-kill-everyone">my blog)</a>. (I’ll be at EAG London this weekend—come say hi. Also, this is my thousandth blogpost—cool milestone!)Several people have wondered why I haven’t written much about AI. The main reason is that I don’t feel that I have anything very original to contribute. I try to only speak about things that I know something about. I don’t really know much about AI. While I use it regularly for editing, and have read a decent amount about it, it’s quite far outside of my area of expertise.But I feel that I should say something about it because it’s important. It’s plausibly the most important thing. We may be approaching a second industrial revolution with consequences more dramatic than the first. There is a real possibility of everyone dying.I’m on the more optimistic side. I think there’s only a few percent chance that AI kills everyone. Maybe 1 or 2%. In the EA circles in which I hang out, this often makes me outrageously optimistic. Lots of people, like Yudkowsky, think we’re nearly guaranteed to all die.But whether one’s P(doom) is 1% or 60%, it’s abundantly clear that we should be doing a lot more than we are currently doing. AI alignment research—research that makes sure AI does what we want—should take up a sizeable portion of the federal budget. AI governance and international treaties are sorely needed. (<a href="https://80000hours.org/career-reviews/">Here are some high impact careers</a>—many related to AI—and here are a bunch of high impact charities for safeguarding the longterm, <a href="https://thingofthings.substack.com/p/ea-funds-that-exist-2024a?utm_source=publication-search">largely by aligning AI</a>). Your odds of dying from AI are a lot higher than your odds of dying in a car accident.If AI goes well, it could usher in unprecedented prosperity. If it goes poorly, it could usher in an unimaginable catastrophe. In such a world, trying to steer AI so that it goes well should be a top global priority.A lot of the arguments for AI risk have been written with a great deal of technical language, but I think the core argument for AI risk is pretty straightforward. We are building things that are much smarter than we are. AI can already do many human jobs better than most people.A few years ago, AI couldn’t write competently. GPT2 was useless. GPT3 was revolutionary. GPT4 was better than humans at many tasks. What will GPT10 look like? Whatever AI is like in 30 years, it will be very impressive.AI has already surpassed us in lots of tasks. The best human chess player can’t hold a candle...

(Crosspost from my blog). (I’ll be at EAG London this weekend—come say hi. Also, this is my thousandth blogpost—cool milestone!) Several people have wondered why I haven’t written much about AI. The main reason is that I don’t feel that I have anything very original to contribute. I try to only speak about things that I know something about. I don’t really know much about AI. While I use it regularly for editing, and have read a decent amount about it, it’s quite far outside of my area of expertise. But I feel that I should say something about it because it’s important. It’s plausibly the most important thing. We may be approaching a second industrial revolution with consequences more dramatic than the first. There is a real possibility of everyone dying. I’m on the more optimistic side. I think there’s only a few percent chance that AI kills everyone. Maybe 1 or 2%. In the EA circles in which I hang out, this often makes me outrageously optimistic. Lots of people, like Yudkowsky, think we’re nearly guaranteed to all die. But whether one’s P(doom) is 1% or 60%, it’s abundantly clear that we should be doing a lot more than we are currently doing. AI alignment research—research that makes sure AI does what we want—should take up a sizeable portion of the federal budget. AI governance and international treaties are sorely needed. (Here are some high impact careers—many related to AI—and here are a bunch of high impact charities for safeguarding the longterm, largely by aligning AI). Your odds of dying from AI are a lot higher than your odds of dying in a car accident. If AI goes well, it could usher in unprecedented prosperity. If it goes poorly, it could usher in an unimaginable catastrophe. In such a world, trying to steer AI so that it goes well should be a top global priority. A lot of the arguments for AI risk have been written with a great deal of technical language, but I think the core argument for AI risk is pretty straightforward. We are building thi

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AISI is being rebranded highly non-confusingly as CAISI. Is it the end of AISI and a huge disaster, or a tactical renaming to calm certain people down? Hard to tell. It could go either way. Sometimes you need to target the people who call things ‘big beautiful bill,’ and hey, the bill in question is indeed big. It contains multitudes.The AI world also contains multitudes. We got Cursor 1.0, time to get coding.On a personal note, this was the week of LessOnline, which was predictably great. I am sad that I could not stay longer, but as we all know, duty calls. Back to the grind. <div> </div> <h4>Table of Contents</h4> <ol> <li><a href="https://thezvi.substack.com/i/164743152/language-models-offer-mundane-utility">Language Models Offer Mundane Utility.</a> The white whale.</li> <li><a href="https://thezvi.substack.com/i/164743152/language-models-don-t-offer-mundane-utility">Language Models Don’t Offer Mundane Utility.</a> You need a system prompt.</li> <li><a href="https://thezvi.substack.com/i/164743152/language-models-could-offer-more-mundane-utility">Language Models Could Offer More Mundane Utility.</a> A good set of asks.</li> <li><a href="https://thezvi.substack.com/i/164743152/huh-upgrades">Huh, Upgrades.</a> The highlight is Cursor 1.0, with memory and more.</li> <li><a href="https://thezvi.substack.com/i/164743152/fun-with-media-generation">Fun With Media Generation.</a> Video is high bandwidth. But also low bandwidth.</li> <li><a href="https://thezvi.substack.com/i/164743152/choose-your-fighter">Choose Your Fighter.</a> Opinions differ, I continue to mostly be on Team Claude.</li> <li><a href="https://thezvi.substack.com/i/164743152/deepfaketown-and-botpocalypse-soon">Deepfaketown and Botpocalypse Soon.</a> Fake is not a natural category. Whoops.</li> <li><a href="https://thezvi.substack.com/i/164743152/get-my-agent-on-the-line">Get My Agent On The Line.</a> We all know they’re not secure, but how bad is this?</li> <li><a href="https://thezvi.substack.com/i/164743152/they-took-our-jobs">They Took Our Jobs.</a> Economists respond to Dario’s warning.</li> <li><a href="https://thezvi.substack.com/i/164743152/the-art-of-the-jailbreak">The Art of the Jailbreak.</a> Why not jailbreak AI overviews?</li> <li><a href="https://thezvi.substack.com/i/164743152/unprompted-attention">Unprompted Attention.</a> More prompts to try out.</li> <li><a href="https://thezvi.substack.com/i/164743152/get-involved">Get Involved.</a> SFCompute, Speculative Technologies.</li> <li><a href="https://thezvi.substack.com/i/164743152/introducing">Introducing.</a> Anthropic open sources interpretability tools, better AR glasses.</li> <li><a href="https://thezvi.substack.com/i/164743152/in-other-ai-news">In Other AI News.</a> FDA launches their AI tool called Elsa.</li> <li><a href="https://thezvi.substack.com/i/164743152/show-me-the-money">Show Me the Money.</a> Delaware hires bank to value OpenAI’s nonprofit.</li> <li><a href="https://thezvi.substack.com/i/164743152/quiet-speculations">Quiet Speculations.</a> People don’t get what is coming, but hey, could be worse.</li> <li><a href="https://thezvi.substack.com/i/164743152/taking-off">Taking Off.</a> AI beats humans in a test of predicting the results of ML experiments.</li> <li><a href="https://thezvi.substack.com/i/164743152/goodbye-aisi">Goodbye AISI?</a> They’re rebranding as CAISI. It’s unclear how much this matters.</li> <li><a href="https://thezvi.substack.com/i/164743152/the-quest-for-sane-regulations">The Quest for Sane Regulations.</a> The bill is, at least, definitely big. Tl;dr.</li> <li><a href="https://thezvi.substack.com/i/164743152/copyright-confrontation">Copyright Confrontation.</a> OpenAI is being forced to retain all its chat logs.</li> <li><a href="https://thezvi.substack.com/i/164743152/differential-access">Differential Access.</a> The Good Guy needs a better AI than the Bad Guy.</li> <li><a href="https://thezvi.substack.com/i/164743152/the-week-in-audio">The Week in Audio.</a> Altman, Tegmark, Amodei, Barnes.</li> <li><a href="https://thezvi.substack.com/i/164743152/when-david-sacks-says-win-the-ai-race-he-literally-means-market-share">When David Sacks Says ‘Win the AI Race’ He Literally Means Market Share.</a></li> <li><a href="https://thezvi.substack.com/i/164743152/rhetorical-innovation">Rhetorical Innovation.</a> Blog metagame continues to dominate.</li> <li><a href="https://thezvi.substack.com/i/164743152/aligning-a-smarter-than-human-intelligence-is-difficult">Aligning a Smarter Than Human Intelligence is Difficult.</a> Proceed accordingly.</li> <li><a href="https://thezvi.substack.com/i/164743152/misaligned">Misaligned!</a> About that safety plan, would it, you know, actually </li></ol>...

AISI is being rebranded highly non-confusingly as CAISI. Is it the end of AISI and a huge disaster, or a tactical renaming to calm certain people down? Hard to tell. It could go either way. Sometimes you need to target the people who call things ‘big beautiful bill,’ and hey, the bill in question is indeed big. It contains multitudes. The AI world also contains multitudes. We got Cursor 1.0, time to get coding. On a personal note, this was the week of LessOnline, which was predictably great. I am sad that I could not stay longer, but as we all know, duty calls. Back to the grind. TABLE OF CONTENTS 1. Language Models Offer Mundane Utility. The white whale. 2. Language Models Don’t Offer Mundane Utility. You need a system prompt. 3. Language Models Could Offer More Mundane Utility. A good set of asks. 4. Huh, Upgrades. The highlight is Cursor 1.0, with memory and more. 5. Fun With Media Generation. Video is high bandwidth. But also low bandwidth. 6. Choose Your Fighter. Opinions differ, I continue to mostly be on Team Claude. 7. Deepfaketown and Botpocalypse Soon. Fake is not a natural category. Whoops. 8. Get My Agent On The Line. We all know they’re not secure, but how bad is this? 9. They Took Our Jobs. Economists respond to Dario’s warning. 10. The Art of the Jailbreak. Why not jailbreak AI overviews? 11. Unprompted Attention. More prompts to try out. 12. Get Involved. SFCompute, Speculative Technologies. 13. Introducing. Anthropic open sources interpretability tools, better AR glasses. 14. In Other AI News. FDA launches their AI tool called Elsa. 15. Show Me the Money. Delaware hires bank to value OpenAI’s nonprofit. 16. Quiet Speculations. People don’t get what is coming, but hey, could be worse. 17. Taking Off. AI beats humans in a test of predicting the results of ML experiments. 18. Goodbye AISI? They’re rebranding as CAISI. It’s unclear how much this matters. 19. The Quest for Sane Regulations. The bill is, at least, definit

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A thoughtful post by Anton Leicht, “<a href="https://writing.antonleicht.me/p/powerless-predictions">Powerless Predictions</a>,” notes that forecasting may be underutilized by AI policy organizations. But how much is forecasting actually being used? Which organizations working on AI—particularly AI safety—already integrate forecasting into their decision-making, policy recommendations, and strategic planning? What forecasting work is being done, and how is it impacting the future of AI?I set out to investigate this.While I hope this post proves useful to others, I wrote it primarily for myself to gain an overview of forecasting work and its impacts. Feel free to skim—only read the details if you find them valuable.<h2>Grantmaking</h2>Open Philanthropy, which funds numerous AI safety initiatives, regularly makes predictions about grant outcomes. Evaluating these outcomes helps them improve their ability to predict the results of future grantmaking decisions. Javier Prieto at Open Philanthropy explains this process and evaluates prediction accuracy in <a href="https://www.openphilanthropy.org/research/how-accurate-are-our-predictions/#id-1-how-we-make-and-check-our-forecasts">this post</a>.They have also commissioned an long list of <a href="https://arbresearch.com/files/clairvoyance.pdf">forecasting questions on AI</a> from <a href="https://arbresearch.com/">Arb Research</a>. I couldn't find any publicly documented uses of this list, though Open Philanthropy may have used it for internal planning and strategic thinking.<h2>AI development</h2>Anthropic <a href="https://www.anthropic.com/research/forecasting-rare-behaviors">extrapolates rare AI behaviors</a> to reduce concerns about concerning behaviors that may be missed during evaluations.In Anthropic's <a href="https://www.anthropic.com/news/anthropics-responsible-scaling-policy">Responsible Scaling Policy</a> (RSP), they incorporate forecasting for comprehensive AI assessment, making informal predictions about improvements of elicitation techniques and enhanced model performance between testing rounds. They aim to "improve these forecasts over time so that they can be relied upon for risk judgments."While you could argue that frontier AI development would be irresponsible without forecasting future capabilities and risks, it's nevertheless encouraging to see this explicitly incorporated into their practices. DeepMind's <a href="https://deepmind.google/discover/blog/updating-the-frontier-safety-framework/">Frontier Safety Framework</a> doesn't appear to explicitly include forecasting, and OpenAI's <a href="https://openai.com/index/updating-our-preparedness-framework/">Preparedness Framework</a> only mentions it in passing.<h2>Affecting Policy Decisions</h2>While policy recommendations implicitly depend on predictions about the future, explicit and well-researched forecasting doesn't appear to be the norm. However, much policy work may be informed by forecasts without being transparently based on forecast analyses—making it difficult to determine how...

A thoughtful post by Anton Leicht, “Powerless Predictions,” notes that forecasting may be underutilized by AI policy organizations. But how much is forecasting actually being used? Which organizations working on AI—particularly AI safety—already integrate forecasting into their decision-making, policy recommendations, and strategic planning? What forecasting work is being done, and how is it impacting the future of AI? I set out to investigate this. While I hope this post proves useful to others, I wrote it primarily for myself to gain an overview of forecasting work and its impacts. Feel free to skim—only read the details if you find them valuable. Grantmaking Open Philanthropy, which funds numerous AI safety initiatives, regularly makes predictions about grant outcomes. Evaluating these outcomes helps them improve their ability to predict the results of future grantmaking decisions. Javier Prieto at Open Philanthropy explains this process and evaluates prediction accuracy in this post. They have also commissioned an long list of forecasting questions on AI from Arb Research. I couldn't find any publicly documented uses of this list, though Open Philanthropy may have used it for internal planning and strategic thinking. AI development Anthropic extrapolates rare AI behaviors to reduce concerns about concerning behaviors that may be missed during evaluations. In Anthropic's Responsible Scaling Policy (RSP), they incorporate forecasting for comprehensive AI assessment, making informal predictions about improvements of elicitation techniques and enhanced model performance between testing rounds. They aim to "improve these forecasts over time so that they can be relied upon for risk judgments." While you could argue that frontier AI development would be irresponsible without forecasting future capabilities and risks, it's nevertheless encouraging to see this explicitly incorporated into their practices. DeepMind's Frontier Safety Framework doesn't appear to ex

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This is a list of projects<a href="#fnlcc7gtz365j">[1]</a> to consider for folks who want to use Wise AI to steer the world towards positive outcomes.Some of these projects are listed because they're impactful. Others are listed because I believe they would be good projects for someone to get started. Please note that this post is titled "potentially useful projects" for a reason. Some of these projects are likely to have much higher impact than others. I expect some are net-negative. Don't ignore your own judgment just because a project is listed here!I'm sure my views about what projects are valuable will change quite significantly as I dive deeper into the topic, but I still think it's worthwhile for me to put some kind of list out there. But first, an announcement:<details class="detailsBlock"><summary class="detailsBlockTitle">Applications for The Future of Life Foundation's Fellowship on AI for Human Reasoning are closing soon (June 9th!) They've listed "Tools for wise decision making" as a possible area to work on. Expand for more details.</summary><div class="detailsBlockContent">From their website:<blockquote>Apply by June 9th | $25k–$50k stipend | 12 weeks, from July 14 - October 3 Join us in working out how to build a future which robustly empowers humans and improves decision-making.  FLF’s incubator fellowship on AI for human reasoning will help talented researchers and builders start working on AI tools for coordination and epistemics. Participants will scope out and work on pilot projects in this area, with discussion and guidance from experts working in related fields. FLF will provide fellows with a $25k–$50k stipend, the opportunity to work in a shared office in the SF Bay Area or remotely, and other support.  In some cases we would be excited to provide support beyond the end of the fellowship period, or help you in launching a new organization.</blockquote><a href="https://www.flf.org/fellowship">Further Information</a> ✦ <a href="https://jobs.lever.co/futureof-life/ffc752f2-a420-4c87-8c58-2212ae2e885c/apply">Apply now!</a></div></details>I was originally going to delay publishing this until after making the case Wise AI Advisors being a priority (and, more generally, Wise AI as well), but I ended up deciding that it was important to publish this before the deadline for the Future of Life Fellowship in case it inspired more people to apply.<hr><h3>Field-building:</h3>At this stage, I consider field-building work around Wise AI as especially high priority. I feel that there’s starting to be some real energy around this area, however, very little of this will aid us in avoiding catastrophic risks. Fields tend to be more flexible in their initi...

This is a list of projects[1] to consider for folks who want to use Wise AI to steer the world towards positive outcomes. Some of these projects are listed because they're impactful. Others are listed because I believe they would be good projects for someone to get started. Please note that this post is titled "potentially useful projects" for a reason. Some of these projects are likely to have much higher impact than others. I expect some are net-negative. Don't ignore your own judgment just because a project is listed here! I'm sure my views about what projects are valuable will change quite significantly as I dive deeper into the topic, but I still think it's worthwhile for me to put some kind of list out there. But first, an announcement: Applications for The Future of Life Foundation's Fellowship on AI for Human Reasoning are closing soon (June 9th!) They've listed "Tools for wise decision making" as a possible area to work on. Expand for more details. From their website: > Apply by June 9th | $25k–$50k stipend | 12 weeks, from July 14 - October 3 > > Join us in working out how to build a future which robustly empowers humans and improves decision-making. > > FLF’s incubator fellowship on AI for human reasoning will help talented researchers and builders start working on AI tools for coordination and epistemics. Participants will scope out and work on pilot projects in this area, with discussion and guidance from experts working in related fields. FLF will provide fellows with a $25k–$50k stipend, the opportunity to work in a shared office in the SF Bay Area or remotely, and other support. > > In some cases we would be excited to provide support beyond the end of the fellowship period, or help you in launching a new organization. Further Information ✦ Apply now! I was originally going to delay publishing this until after making the case Wise AI Advisors being a priority (and, more generally, Wise AI as well), but I ended up deciding that it wa

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Since I’ve been interviewing other builders for the <a href="https://nichedesign.press/">niche design zine</a>, I've noticed a few recurring patterns. One is “building as gardening” which seems like a healthier way to build software in our age.This is how Cab of <a href="https://www.are.na/">Are.na</a> described to me this approach:<blockquote>You can be like “we’ve got to plant these here!” or “this is not working, let’s take it out!”.But you can also choose the other way which is more like “oh wow, this thing is growing, that's really cool” or “let's see what else we can put here”.It's a different kind of perspective.</blockquote>And this, unrelatedly or not, how Herman of <a href="https://bearblog.dev/">Bear Blog</a> put it:<blockquote>I’m trying to build a similar product with <a href="https://bearblog.dev/">Bear Blog</a>. Something niche but valuable. Something I can spend time on because I want to.Being able to talk to, and interact with the people using my tools is fulfilling. Spending time meticulously improving subtle aspects of the product is enjoyable.<a href="https://herman.bearblog.dev/my-product-is-my-garden/">My product is my garden</a></blockquote>I’m no poetic person, so I admit this metaphor was a bit hard to digest at first. However, after pondering on this perspective for a while, I suddenly could make sense out of it.For the longest time, I’ve seen people building software like in a greenhouse mode: carefully controlling and forcing conditions to make something grow. That often means building more “necessary” features, paying people to participate in “research” surveys, committing early to investors, or rushing to catch the “right moment” in the market.The common thread of this build-fast-break-things-pivot-repeat culture is the sense of urgency. There’s a collective pressure to grow something faster than it naturally would. Instead of letting things grow more organically, we’re forced to grow them more artificially. Then everything feels urgent.But that's just one way to view this world.Then there's the gardening approach, which is a different process; a different mindset. Walking this path isn’t about the classic solving problems mantra through countless plans, strategies, and checklists to mark. Nor is it about taking the hammer and knocking a few components and forcing them to fit together.Gardening is rather about tending something slowly until it (or not) reaches any height, while giving it the necessary time to mature and evolve. It’s not about forcing a solution, but rather about allowing growth to happen more organically.Building software is much the same. It’s not a science-ba...

Since I’ve been interviewing other builders for the niche design zine, I've noticed a few recurring patterns. One is “building as gardening” which seems like a healthier way to build software in our age. This is how Cab of Are.na described to me this approach: > You can be like “we’ve got to plant these here!” or “this is not working, let’s take it out!”. > > But you can also choose the other way which is more like “oh wow, this thing is growing, that's really cool” or “let's see what else we can put here”. > > It's a different kind of perspective. And this, unrelatedly or not, how Herman of Bear Blog put it: > I’m trying to build a similar product with Bear Blog. Something niche but valuable. Something I can spend time on because I want to. > > Being able to talk to, and interact with the people using my tools is fulfilling. Spending time meticulously improving subtle aspects of the product is enjoyable. > > My product is my garden I’m no poetic person, so I admit this metaphor was a bit hard to digest at first. However, after pondering on this perspective for a while, I suddenly could make sense out of it. For the longest time, I’ve seen people building software like in a greenhouse mode: carefully controlling and forcing conditions to make something grow. That often means building more “necessary” features, paying people to participate in “research” surveys, committing early to investors, or rushing to catch the “right moment” in the market. The common thread of this build-fast-break-things-pivot-repeat culture is the sense of urgency. There’s a collective pressure to grow something faster than it naturally would. Instead of letting things grow more organically, we’re forced to grow them more artificially. Then everything feels urgent. But that's just one way to view this world. Then there's the gardening approach, which is a different process; a different mindset. Walking this path isn’t about the classic solving problems mantra through countless pl

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An in-depth look at semiconductor processing equipment: considerations, selection, operation, and supporting info.<h2>Preface</h2>I tried to include as many links as possible to allow the reader to go down rabbit holes as they see fit.I also tried to keep info basic enough for the skimmers/laypeople to enjoy while still adding more technical details located at the end of each section in the collapsible element. Pictures are sprinkled throughout to put an image with the words.Interactive animations are denoted by the large "Click here to [X]!". While rudimentary, they get the job done. These were vibe coded using Claude 4 Opus with extended thinking and a few iterations.This information is current as of May 2025.<hr><h2>Context</h2>Before reading this, you should read Construction Physics' (Brian Potter) <a href="https://www.construction-physics.com/p/how-to-build-a-20-billion-semiconductor">How to Build a $20 Billion Semiconductor Fab</a>. It's an excellent introduction into both the semiconductor manufacturing process and the physical building that houses the manufacturing operations. However, Brian (understandably) chooses not to expand on a critical component: the equipment used to make the semiconductor chips.As Brian writes:<blockquote>Early integrated circuits could be made with just ... dozens of process steps, but a modern leading-edge microchip might require ... thousands of separate process steps.</blockquote>These individual process steps are performed by semiconductor equipment, also called tools or machines. In the same way that a traditional machine shop has a mill, lathe, and bandsaw to perform a variety of operations on a single piece of metal to create a final product, a fab has deposition tools, etch tools, and lithography tools (among many, many others) to perform a variety of operations on a single silicon wafer to create the final chip.There are 100s to 1000s of tools in a single fab, making it important to use the available space in an efficient manner. Some tools are quite boxy to help minimize the area used, or the footprint. Some tools are less boxy due to sheer requirements, i.e., the original equipment manufacturer (OEM) could only make it so small area-wise before it lost some functionality or performance. OEMs are incentivized to minimize footprint so companies will buy more of their tools. Large-scale OEMs include Applied Materials (AMAT), Tokyo Electron Limited (TEL), and LAM Research.Here's a picture of an AMAT tool, the Endura. Note that while not perfectly box-l...

An in-depth look at semiconductor processing equipment: considerations, selection, operation, and supporting info. Preface I tried to include as many links as possible to allow the reader to go down rabbit holes as they see fit. I also tried to keep info basic enough for the skimmers/laypeople to enjoy while still adding more technical details located at the end of each section in the collapsible element. Pictures are sprinkled throughout to put an image with the words. Interactive animations are denoted by the large "Click here to [X]!". While rudimentary, they get the job done. These were vibe coded using Claude 4 Opus with extended thinking and a few iterations. This information is current as of May 2025. ---------------------------------------- Context Before reading this, you should read Construction Physics' (Brian Potter) How to Build a $20 Billion Semiconductor Fab. It's an excellent introduction into both the semiconductor manufacturing process and the physical building that houses the manufacturing operations. However, Brian (understandably) chooses not to expand on a critical component: the equipment used to make the semiconductor chips. As Brian writes: > Early integrated circuits could be made with just ... dozens of process steps, but a modern leading-edge microchip might require ... thousands of separate process steps. These individual process steps are performed by semiconductor equipment, also called tools or machines. In the same way that a traditional machine shop has a mill, lathe, and bandsaw to perform a variety of operations on a single piece of metal to create a final product, a fab has deposition tools, etch tools, and lithography tools (among many, many others) to perform a variety of operations on a single silicon wafer to create the final chip. There are 100s to 1000s of tools in a single fab, making it important to use the available space in an efficient manner. Some tools are quite boxy to help minimize the area used, or the

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I want to define a new term that will be useful in the discourse.Imagine you landed in a foreign country with dogs, but with no word for dogs. You would see these things, and want to talk about them. You would like to be able to tell people whether they can bring dogs into your house, but you can’t, because there isn’t any word for “dog.” You’d like to ask if you can pet someone’s dog, but you can’t do that either, because when you ask “can I pet your dog?” they say “pet my what?” It would get pretty frustrating.This is how I feel about a certain phenomenon I’ve noticed over the last year or so. I notice it all the time—It’s as ubiquitous in society as dogs are, and I see it just about as often—but there isn’t a word for it. People seem to make the same error of judgement over and over, and I want to talk about this error. There’s a lot I want to say about it. I want to be able to point out when someone is making the mistake. I want to ask people about whether I’m making the mistake. I want to talk about how some people seem to see very clearly, and make the mistake very rarely. Others seem to be very cloudy-eyed where this mistake is concerned, and they make it very often.So, I’m going to make up a new term. The term is “The Stereotype of the Stereotype.” The mistake I’m talking about is not “The Stereotype of the Stereotype.” The Stereotype of the Stereotype is a noun. This mistake is a verb. The verb is “confusing the stereotype with The Stereotype of the Stereotype.”Mixing up ‘the stereotype’ with ‘The Stereotype of the Stereotype’ is as significant a mistake as seeing the word “rainstorm,” and opening your umbrella. You aren’t allowed to mix up levels that way. The stereotype is the stereotype. The Stereotype of the The Stereotype is The Stereotype of the The Stereotype. And they are as different as a rainstorm, and the word “Rainstorm.”Let’s use some examples to define this term “The Stereotype of the Stereotype.” - Sir Terrence Pratchett was a British satirical fantasy author, most famous for his Discworld series of books.There are 41 of these books. Most of them follow distinct arrangements of characters. For example, eight of them follow a man named Sam Vimes. Another five follow a girl named Tiffany Aching. However, there’s one character who shows up in nearly every book. His name is Death.Death appears in 39 of the 41...

I want to define a new term that will be useful in the discourse. Imagine you landed in a foreign country with dogs, but with no word for dogs. You would see these things, and want to talk about them. You would like to be able to tell people whether they can bring dogs into your house, but you can’t, because there isn’t any word for “dog.” You’d like to ask if you can pet someone’s dog, but you can’t do that either, because when you ask “can I pet your dog?” they say “pet my what?” It would get pretty frustrating. This is how I feel about a certain phenomenon I’ve noticed over the last year or so. I notice it all the time—It’s as ubiquitous in society as dogs are, and I see it just about as often—but there isn’t a word for it. People seem to make the same error of judgement over and over, and I want to talk about this error. There’s a lot I want to say about it. I want to be able to point out when someone is making the mistake. I want to ask people about whether I’m making the mistake. I want to talk about how some people seem to see very clearly, and make the mistake very rarely. Others seem to be very cloudy-eyed where this mistake is concerned, and they make it very often. So, I’m going to make up a new term. The term is “The Stereotype of the Stereotype.” The mistake I’m talking about is not “The Stereotype of the Stereotype.” The Stereotype of the Stereotype is a noun. This mistake is a verb. The verb is “confusing the stereotype with The Stereotype of the Stereotype.” Mixing up ‘the stereotype’ with ‘The Stereotype of the Stereotype’ is as significant a mistake as seeing the word “rainstorm,” and opening your umbrella. You aren’t allowed to mix up levels that way. The stereotype is the stereotype. The Stereotype of the The Stereotype is The Stereotype of the The Stereotype. And they are as different as a rainstorm, and the word “Rainstorm.” Let’s use some examples to define this term “The Stereotype of the Stereotype.” - Sir Terrence Pratchett was

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Anthony DiGiovanni

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Researcher at the Center on Long-Term Risk. All opinions my own.

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We’ve seen <a href="https://forum.effectivealtruism.org/posts/a3hnfA9EnYm9bssTZ/1-the-challenge-of-unawareness-for-impartial-altruist-action-1">so far</a> that unawareness leaves us with gaps in the impartial altruistic justification for our decisions. It’s fundamentally unclear how the possible outcomes of our actions trade off against each other.But perhaps this ambiguity doesn’t matter much in practice, since the consequences that predominantly shape the impartial value of the future (“large-scale consequences”, for short) seem at least somewhat foreseeable. Here are two ways we might think we can fill these gaps:<ol><li>Implicit: “Even if we can’t assign EVs to interventions, we have reason to trust that our pre-theoretic intuitions at least track an intervention’s sign. These intuitions can distinguish whether the large-scale consequences are net-better than inaction.”<a href="#fnhlpvuxlj9e">[1]</a></li><li>Explicit: “True, we don’t directly conceive of fine-grained possible worlds, only coarse hypotheses. We can still assign precise EVs, though, by intuitively estimating the average value of the possible worlds in each hypothesis. These best-guess estimates will at least be better than chance.”</li></ol>Do these approaches hold up, when we look concretely at how vast our unawareness could be? “The far future is complex” is a truism that many EAs are probably sick of hearing. But here I’ll argue that this complexity — which also plagues our impact on near-term pivotal events like AI takeoff — specifically undermines both Implicit and Explicit. For instance, we can’t simply say, “It’s intuitively clear that trying to spread altruistic values would be better than doing nothing”, nor, “If it’s better for future agents to be altruistic, we should try to spread altruism, since intuitively this is at least somewhat more likely to succeed than backfire”. This is because our intuitions about large-scale consequences don’t seem to account for unawareness with enough precision. If that’s true, we’ll need a new framework for impartially evaluating our actions under unawareness.The argument in brief: First, a strategy’s sign from an impartial perspective seems unusually sensitive to factors we’re unaware of, compared to the local perspective we take in more familiar decision problems (cf. Roussos (2021)). Thus it’s unsurprising if our intuitions can justify claims like “A is net-positive” on the local scale, yet don’t justify such claims on the cosmic scale. 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We’ve seen so far that unawareness leaves us with gaps in the impartial altruistic justification for our decisions. It’s fundamentally unclear how the possible outcomes of our actions trade off against each other. But perhaps this ambiguity doesn’t matter much in practice, since the consequences that predominantly shape the impartial value of the future (“large-scale consequences”, for short) seem at least somewhat foreseeable. Here are two ways we might think we can fill these gaps: 1. Implicit: “Even if we can’t assign EVs to interventions, we have reason to trust that our pre-theoretic intuitions at least track an intervention’s sign. These intuitions can distinguish whether the large-scale consequences are net-better than inaction.”[1] 2. Explicit: “True, we don’t directly conceive of fine-grained possible worlds, only coarse hypotheses. We can still assign precise EVs, though, by intuitively estimating the average value of the possible worlds in each hypothesis. These best-guess estimates will at least be better than chance.” Do these approaches hold up, when we look concretely at how vast our unawareness could be? “The far future is complex” is a truism that many EAs are probably sick of hearing. But here I’ll argue that this complexity — which also plagues our impact on near-term pivotal events like AI takeoff — specifically undermines both Implicit and Explicit. For instance, we can’t simply say, “It’s intuitively clear that trying to spread altruistic values would be better than doing nothing”, nor, “If it’s better for future agents to be altruistic, we should try to spread altruism, since intuitively this is at least somewhat more likely to succeed than backfire”. This is because our intuitions about large-scale consequences don’t seem to account for unawareness with enough precision. If that’s true, we’ll need a new framework for impartially evaluating our actions under unawareness. The argument in brief: First, a strategy’s sign from an impartial

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Previously: <a href="https://thezvi.substack.com/p/dating-roundup-1-this-is-why-youre?utm_source=publication-search">#1</a>, <a href="https://thezvi.substack.com/p/dating-roundup-2-if-at-first-you?utm_source=publication-search">#2</a>, <a href="https://thezvi.substack.com/p/dating-roundup-3-third-times-the?utm_source=publication-search">#3</a>, #4, #5Dating Roundup #4 covered dating apps. Roundup #5 covered opening without them.Dating Roundup #6 covers everything else. <h4>Table of Contents</h4> <ol> <li><a href="https://thezvi.substack.com/i/160142469/you-re-single-because-you-can-t-handle-basic-logistics">You’re Single Because You Can’t Handle Basic Logistics.</a></li> <li><a href="https://thezvi.substack.com/i/160142469/you-re-single-because-you-don-t-ask-questions">You’re Single Because You Don’t Ask Questions.</a></li> <li><a href="https://thezvi.substack.com/i/160142469/you-re-single-because-of-your-terrible-dating-tactics">You’re Single Because of Your Terrible Dating Tactics.</a></li> <li><a href="https://thezvi.substack.com/i/160142469/you-re-single-because-you-refuse-to-play-your-role">You’re Single Because You Refuse to Play Your Role.</a></li> <li><a href="https://thezvi.substack.com/i/160142469/you-re-single-because-people-are-crazy-about-age-gaps">You’re Single Because People Are Crazy About Age Gaps.</a></li> <li><a href="https://thezvi.substack.com/i/160142469/you-re-single-and-you-need-professional-help">You’re Single and You Need Professional Help.</a></li> <li><a href="https://thezvi.substack.com/i/160142469/you-re-single-because-you-never-close">You’re Single Because You Never Close.</a></li> <li><a href="https://thezvi.substack.com/i/160142469/you-re-single-because-you-re-bad-at-sex-and-everyone-knows">You’re Single Because You’re Bad at Sex And Everyone Knows.</a></li> <li><a href="https://thezvi.substack.com/i/160142469/you-re-single-because-you-are-only-a-fan">You’re Single Because You Are Only a Fan.</a></li> <li><a href="https://thezvi.substack.com/i/160142469/you-re-single-because-of-preference-falsification">You’re Single Because of Preference Falsification.</a> <div> </div> </li> <li><a href="https://thezvi.substack.com/i/160142469/you-re-single-because-you-have-insufficient-visual-aids">You’re Single Because You Have Insufficient Visual Aids.</a></li> <li><a href="https://thezvi.substack.com/i/160142469/you-re-single-because-you-told-your-partner-you-didn-t-want-them">You’re Single Because You Told Your Partner You Didn’t Want Them.</a></li> <li><a href="https://thezvi.substack.com/i/160142469/you-re-single-because-of-your-terrible-dating-strategy">You’re Single Because of Your Terrible Dating Strategy.</a></li> <li><a href="https://thezvi.substack.com/i/160142469/you-re-single-because-you-don-t-enjoy-the-process">You’re Single Because You Don’t Enjoy the Process.</a></li> <li><a href="https://thezvi.substack.com/i/160142469/you-re-single-because-you-don-t-escalate-quickly">You’re Single Because You Don’t Escalate Quickly.</a></li> <li><a href="https://thezvi.substack.com/i/160142469/you-re-single-because-your-standards-are-too-high">You’re Single Because Your Standards Are Too High.</a></li> <li><a href="https://thezvi.substack.com/i/160142469/you-re-single-because-you-read-the-wrong-books">You’re Single Because You Read the Wrong Books.</a></li> <li><a href="https://thezvi.substack.com/i/160142469/you-re-single-because-you-re-short-sorry-that-s-all-there-is-to-it">You’re Single Because You’re Short, Sorry, That’s All There Is To It.</a></li> <li><a href="https://thezvi.substack.com/i/160142469/you-re-single-because-of-bad-government-incentives">You’re Single Because of Bad Government Incentives.</a></li> <li><a href="https://thezvi.substack.com/i/160142469/you-re-single-because-you-don-t-realize-cheating-is-wrong">You’re Single Because You Don’t Realize Cheating is Wrong.</a></li> <li><a href="https://thezvi.substack.com/i/160142469/you-re-single-because-you-re-doing-polyamory-wrong">You’re Single Because You’re Doing Polyamory Wrong.</a></li> <li><a href="https://thezvi.substack.com/i/160142469/you-re-single-because-you-don-t-beware-cheaters">You’re Single Because You Don’t Beware Cheaters.</a></li> <li><a href="https://thezvi.substack.com/i/160142469/you-re-single-because-your-ex-spilled-the-tea">You’re Single Because Your Ex Spilled the Tea.</a></li> <li><a href="https://thezvi.substack.com/i/160142469/you-re-single-because-you-re-assigning-people-numbers">You’re Single Because You’re Assigning People Numbers.</a></li> <li><a href="https://thezvi.substack.com/i/160142469/you-re-single-because-you-are-the-wrong-amount-of-kinky">You’re Single Because You Are The Wrong Amount of Kinky.</a></li> <li><a href="https://thezvi.substack.com/i/160142469/you-re-single-because-you-re-not-good-enough-at-sex">You’re Single Because You’re Not Good Enough at Sex.</a></li> <li><a href="https://thezvi.substack.com/i/160142469/you-re-single-but-not-because-of-your-bodycount">You’re Single But Not Because of Your Bodycount.</a></li> <li><a href="https://thezvi.substack.com/i/160142469/you-re-single-because-they-divorced-you">You’re Single Because They Divorced You.</a></li> <li><a href="https://thezvi.substack.com/i/160142469/you-re-single-because-no-one-tells-you-anything">You’re Single Because No One Tells You Anything.</a></li> <li><a href="https://thezvi.substack.com/i/160142469/you-re-single-and-you-re-not-alone">You’re Single And You’re Not Alone.</a></li> <li><a href="https://thezvi.substack.com/i/160142469/you-re-single-because-things-are-steadily-getting-worse">You’re Single Because Things Are Steadily Getting Worse.</a></li> <li><a href="https://thezvi.substack.com/i/160142469/you-re-single-because-you-didn-t-go-to-college">You’re Single Because You Didn’t Go to College.</a></li> <li><a href="https://thezvi.substack.com/i/160142469/you-re-single-but-this-isn-t-about-you">You’re Single But This Isn’t About You.</a></li> <li><a href="https://thezvi.substack.com/i/160142469/you-re-single-so-let-s-go-to-the-videotape">You’re Single so Let’s Go to the Videotape.</a></li> <li><a href="https://thezvi.substack.com/i/160142469/you-re-single-because-you-don-t-seek-out-good-advice">You’re Single Because You Don’t Seek Out Good Advice.</a></li> <li><a href="https://thezvi.substack.com/i/160142469/you-re-single-so-here-s-some-hope">You’re Single So Here’s Some Hope.</a></li> </ol> <h4>You’re Single Because You Can’t Handle Basic Logistics</h4> You can take the pressure off yourself to plan the perfect date.<a href="https://x.com/senatorshoshana/status/1859743495977685019">Instead, plan any date at all.</a> <blockquote>Shoshana Weissmann: I was hanging out with my married friend, who is a great father and husband, telling him how men cannot plan dates. He said, “What’s there to plan? They pick a bar, a restaurant, and a park for a walk nearby.” And </blockquote>...

Previously: #1, #2, #3, #4, #5 Dating Roundup #4 covered dating apps. Roundup #5 covered opening without them. Dating Roundup #6 covers everything else. TABLE OF CONTENTS 1. You’re Single Because You Can’t Handle Basic Logistics. 2. You’re Single Because You Don’t Ask Questions. 3. You’re Single Because of Your Terrible Dating Tactics. 4. You’re Single Because You Refuse to Play Your Role. 5. You’re Single Because People Are Crazy About Age Gaps. 6. You’re Single and You Need Professional Help. 7. You’re Single Because You Never Close. 8. You’re Single Because You’re Bad at Sex And Everyone Knows. 9. You’re Single Because You Are Only a Fan. 10. You’re Single Because of Preference Falsification. 11. You’re Single Because You Have Insufficient Visual Aids. 12. You’re Single Because You Told Your Partner You Didn’t Want Them. 13. You’re Single Because of Your Terrible Dating Strategy. 14. You’re Single Because You Don’t Enjoy the Process. 15. You’re Single Because You Don’t Escalate Quickly. 16. You’re Single Because Your Standards Are Too High. 17. You’re Single Because You Read the Wrong Books. 18. You’re Single Because You’re Short, Sorry, That’s All There Is To It. 19. You’re Single Because of Bad Government Incentives. 20. You’re Single Because You Don’t Realize Cheating is Wrong. 21. You’re Single Because You’re Doing Polyamory Wrong. 22. You’re Single Because You Don’t Beware Cheaters. 23. You’re Single Because Your Ex Spilled the Tea. 24. You’re Single Because You’re Assigning People Numbers. 25. You’re Single Because You Are The Wrong Amount of Kinky. 26. You’re Single Because You’re Not Good Enough at Sex. 27. You’re Single But Not Because of Your Bodycount. 28. You’re Single Because They Divorced You. 29. You’re Single Because No One Tells You Anything. 30. You’re Single And You’re Not Alone. 31. You’re Single Because Things Are Steadily Getting Worse. 32. You’re Single Because You Didn’t Go to College. 33

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This is a proposal for a replacement to the traditional Gregorian calendar, which was adopted by Western cultures in 1582 to replace the Julian calendar from Roman times. I submit this proposal for feedback, suggestions, and inquiry into prior proposals of a similar or identical nature.Below is a reproduction of <a href="https://github.com/rickhull/compsci/blob/master/RationalPrimeCalendar.md">https://github.com/rickhull/compsci/blob/master/RationalPrimeCalendar.md</a> <h1>Rational Prime Calendar</h1> A rational basis for a calendar upon 61 day periodsThe Gregorian calendar is a 400-year-old hack job. February's weird. July-August breaks the pattern, and no one can remember which months have 31 days. But there's a better way, hidden in prime factorization. <h2>Considerations</h2> <ul> <li>Solar day (roughly 24 hours for the earth to rotate)</li> <li>Solar year (365.2425 solar days for the earth to orbit the sun)</li> <li>Lunar cycle (29.53 days for 8 distinct moon phases, roughly 12x annually)</li> </ul> It's impossible to coordinate or synchronize these 3 cycles perfectly. But we can devise a system to accommodate them and minimize tradeoffs. We also want to consider historical calendars, particularly the current Gregorian calendar and less so the prior Julian calendar. <h2>Approach</h2> If a solar year is considered to be 366 days, this factors to 61 * 3 * 2, which naturally suggests (6) 61-day periods, or alternately (61) 6-day periods. Perhaps we should have 6 day weeks, but that will be for another proposal.We can split each 61-day period into alternating months of 30 and 31 days, in either order. This provides some alignment with the lunar cycle as well as traditional calendars. <h3>The Prime Insight</h3> <ul> <li>61 is prime; indivisible, mathematically fundamental</li> <li>Each of 6 periods of 61 days can be split into pairs of months, 30 + 31</li> <li>12 months matches the lunar cycle of roughly 30 days</li> <li>12 months allows clean divisibility by 4 (seasons, business quarters)</li> <li>12 months matches tradition</li> <li>Resist entropy: a regular pattern of pairs in a predictable order</li> </ul> <h3>Leap Year</h3> We can retain the Gregorian approach to leap years, which solves the problem of accounting for the remaining 0.2425 days in a solar year as years go by. We'll pick one month out of twelve that will have an extra day roughly every 4 years. If 366 days is our starting basis, then the leap month will have a one-day deficit in most years. <h2>Specifics</h2> For explication, a leap year is considered the base case, and a "normal year" is handled specially, in some sense, ...

This is a proposal for a replacement to the traditional Gregorian calendar, which was adopted by Western cultures in 1582 to replace the Julian calendar from Roman times. I submit this proposal for feedback, suggestions, and inquiry into prior proposals of a similar or identical nature. Below is a reproduction of https://github.com/rickhull/compsci/blob/master/RationalPrimeCalendar.md Rational Prime Calendar A rational basis for a calendar upon 61 day periods The Gregorian calendar is a 400-year-old hack job. February's weird. July-August breaks the pattern, and no one can remember which months have 31 days. But there's a better way, hidden in prime factorization. Considerations * Solar day (roughly 24 hours for the earth to rotate) * Solar year (365.2425 solar days for the earth to orbit the sun) * Lunar cycle (29.53 days for 8 distinct moon phases, roughly 12x annually) It's impossible to coordinate or synchronize these 3 cycles perfectly. But we can devise a system to accommodate them and minimize tradeoffs. We also want to consider historical calendars, particularly the current Gregorian calendar and less so the prior Julian calendar. Approach If a solar year is considered to be 366 days, this factors to 61 * 3 * 2, which naturally suggests (6) 61-day periods, or alternately (61) 6-day periods. Perhaps we should have 6 day weeks, but that will be for another proposal. We can split each 61-day period into alternating months of 30 and 31 days, in either order. This provides some alignment with the lunar cycle as well as traditional calendars. The Prime Insight * 61 is prime; indivisible, mathematically fundamental * Each of 6 periods of 61 days can be split into pairs of months, 30 + 31 * 12 months matches the lunar cycle of roughly 30 days * 12 months allows clean divisibility by 4 (seasons, business quarters) * 12 months matches tradition * Resist entropy: a regular pattern of pairs in a predictable order Leap Year We can retain the Gregor

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Looking a little ahead into the future, I think LLMs are going to stop being focused on knowledgeable, articulate<a href="#fnj9lzohw3u7r">[1]</a> chatbots, but instead be more efficient models that are weaker in these areas than current models, but relatively stronger at reasoning, a pure-reasoner model. The rest will be bolted on via tool-use and other scaffolding<a href="#fnmvhxbtiks0m">[2]</a>.<h1>The Current Idiom is Inefficient</h1>Chatbots make a great replacement for Google searches because they know a lot about everything. But that knowledge comes at a price. The majority of an LLM's parameters are thought to be spent on storing factual knowledge. Parameters are the key determinant of training and inference costs. It likely has a significant cost on data efficiency, and it's possible <a href="https://arxiv.org/abs/2504.03635">these extra parameters just hurt generalisation</a> overall.Memorisation is just not a feature that we need models to have when there are likely more efficient ways to get nearly the same capabilities.We keep building bigger models which memorise more and more data largely because we don't know how not to. Every sentence of pre-training simultaneously encourages the model to memorise more factual information, and ekes it towards better high-level thinking and eventually reasoning ability.But I think that technicality will fall soon, driving towards sleeker agents that fit inside a larger framework<a href="#fn3df166lenhb">[3]</a>.<h1>The Technical Path to Pure Reason</h1>The main blocker at the moment is that we don't know how to train models that have good capability without also training them to be great memorisers. There's also a lesser blocker of ensuring that such a model is reliably supplied in context with any information it needs, to compensate for not being able to furnish it itself.<h2>Decoupling Reasoning Capability from Memorisation</h2>While I cannot predict exactly what techniques are likely to work, it's clear there are several productive areas of current research that all lead in the same direction.Reasoning RL. While I've been using "reasoning" in the colloquial sense, the biggest recent advance in reasoning capabilities comes from reinforcement learning techniques that encourage a reasoning style chain-of-thoughts. This technique is still new enough that there are likely remaining gains<a href="#fn0vumadpafsw">[4]</a>. Reasoning models often have very long thinking traces, which makes all those parameters dedicated to memorisation even more expensive.Mixture of Experts can be viewed as an...

Looking a little ahead into the future, I think LLMs are going to stop being focused on knowledgeable, articulate[1] chatbots, but instead be more efficient models that are weaker in these areas than current models, but relatively stronger at reasoning, a pure-reasoner model. The rest will be bolted on via tool-use and other scaffolding[2]. The Current Idiom is Inefficient Chatbots make a great replacement for Google searches because they know a lot about everything. But that knowledge comes at a price. The majority of an LLM's parameters are thought to be spent on storing factual knowledge. Parameters are the key determinant of training and inference costs. It likely has a significant cost on data efficiency, and it's possible these extra parameters just hurt generalisation overall. Memorisation is just not a feature that we need models to have when there are likely more efficient ways to get nearly the same capabilities. We keep building bigger models which memorise more and more data largely because we don't know how not to. Every sentence of pre-training simultaneously encourages the model to memorise more factual information, and ekes it towards better high-level thinking and eventually reasoning ability. But I think that technicality will fall soon, driving towards sleeker agents that fit inside a larger framework[3]. The Technical Path to Pure Reason The main blocker at the moment is that we don't know how to train models that have good capability without also training them to be great memorisers. There's also a lesser blocker of ensuring that such a model is reliably supplied in context with any information it needs, to compensate for not being able to furnish it itself. Decoupling Reasoning Capability from Memorisation While I cannot predict exactly what techniques are likely to work, it's clear there are several productive areas of current research that all lead in the same direction. Reasoning RL. While I've been using "reasoning" in the colloqu

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<a href="https://twitter.com/ChrisChipMonk">@ChrisChipMonk</a><a href="https://locallyoptimal.blog/">LocallyOptimal.blog</a>For hire: <a href="http://incentivealigned.com/">IncentiveAligned.com</a>

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Has someone you know ever had a “breakthrough” from coaching, meditation, or psychedelics — only to later have it fade?<figure class="image image_resized" style="width:69.13%"><img src="https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/bqPY63oKb8KZ4x4YX/wdx8kgzhus8ogvd4sub9" alt=" Ulisse Mini @MiniUlisse after my @jhanatech retreat I was like "I'm never going to be depressed again!" then proceeded to get depressed again because I was no longer meditating 8hrs/day isolated from everything in my life, lol 2:42 PM · May 10, 2025 · 23.9K Views "><figcaption><a href="https://x.com/MiniUlisse/status/1921319881116676600">Show tweet</a></figcaption></figure>For example, many people experience ego deaths that can last days or sometimes months. But as it turns out, having a sense of self can serve important functions (try navigating a world that expects you to have opinions, goals, and boundaries when you genuinely feel you have none) and finding a better cognitive strategy without downsides is non-trivial. Because the “breakthrough” wasn’t integrated with the conflicts of everyday life, it fades. I call these instances “flaky breakthroughs.”It’s well-known that flaky breakthroughs are common with psychedelics and meditation, but apparently it’s not well-known that flaky breakthroughs are pervasive in coaching and retreats. For example, it is common for someone to do some coaching, feel a “breakthrough”, think, “Wow, everything is going to be different from now on,” but feel and act no differently weeks or months later. Worse, some techniques can even cause bypassing. Such “false breakthroughs” can come with intense positive affect or “cathartic” crying without addressing the underlying issue. (More below.) Flaky breakthroughs can set people back for years or decades: If someone has a “breakthrough” that unexpectedly reverts, they can become jaded on progress itself. They can learn helplessness and give up on growing. The most depressed person you know has likely had this happen multiple times.<figure class="image image_resized" style="width:77.98%"><img src="https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/bqPY63oKb8KZ4x4YX/rszmemxncughvusujkjt" alt=" QC @QiaochuYuan yes, maybe 3-4 times now, various durations. the stories are too long to summarize but i think basically the mechanism was "aha! i've figured out all my problems and now i will never fail ever again" which felt good but was never robust and broke down the next time i failed Quote Chris Lakin @ChrisChipMonk · Aug 29, 2024 Have you ever had Big psychological growth that lasts 1+ months and then basically reverts? Please share your story I'm trying to figure out how common this is 7:28 PM · Aug 29, 2024 · 5,857 Views"><figcaption><a href="https://x.com/QiaochuYuan/status/1829345301397991815">Show tweet</a></figcaption></figure>Flaky breakthroughs pervade inner work. Despite this, almost no one — coaches, therapists, retreats, bodyworkers, etc. — tracks whether their breakthroughs last.<h1>Almost no practitioners track whether breakthroughs last. </h1>Earlier this year, I attempted to make a list of “<a href="https://chrislakin.blog/p/10x-coaches">10x Coaches</a>” to refer people to. 20–30 coaches reached out as interested in working with me, and I asked each to share the best evidence that they had facilitated lasting growth for others.But all anyone could show me were testimonials that basically read, “The session I just had was *really* nice. They had such a kind presence! I felt a big release at the end.” — And I’m glad to hear they’re nice, but immediate reviews do not distinguish lasting growth from flaky breakthroughs.To show you just how bad it can be, one coach asked me how it was even possible to know if the cli...

Has someone you know ever had a “breakthrough” from coaching, meditation, or psychedelics — only to later have it fade? Show tweet For example, many people experience ego deaths that can last days or sometimes months. But as it turns out, having a sense of self can serve important functions (try navigating a world that expects you to have opinions, goals, and boundaries when you genuinely feel you have none) and finding a better cognitive strategy without downsides is non-trivial. Because the “breakthrough” wasn’t integrated with the conflicts of everyday life, it fades. I call these instances “flaky breakthroughs.” It’s well-known that flaky breakthroughs are common with psychedelics and meditation, but apparently it’s not well-known that flaky breakthroughs are pervasive in coaching and retreats. For example, it is common for someone to do some coaching, feel a “breakthrough”, think, “Wow, everything is going to be different from now on,” but feel and act no differently weeks or months later. Worse, some techniques can even cause bypassing. Such “false breakthroughs” can come with intense positive affect or “cathartic” crying without addressing the underlying issue. (More below.) Flaky breakthroughs can set people back for years or decades: If someone has a “breakthrough” that unexpectedly reverts, they can become jaded on progress itself. They can learn helplessness and give up on growing. The most depressed person you know has likely had this happen multiple times. Show tweet Flaky breakthroughs pervade inner work. Despite this, almost no one — coaches, therapists, retreats, bodyworkers, etc. — tracks whether their breakthroughs last. Almost no practitioners track whether breakthroughs last. Earlier this year, I attempted to make a list of “10x Coaches” to refer people to. 20–30 coaches reached out as interested in working with me, and I asked each to share the best evidence that they had facilitated lasting growth for others. But all anyone could s

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LessOnline saved my life. Now how do I let go of this house?

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I was at LessOnline, it was awesome.  When I got home, I discovered that there was a shooting (two random people were violently attacked by a gang of '4-6 people') in a parking lot near my house Sunday evening. That parking lot serves two of my favorite restaurants and my preferred bank. Looking through my location history, there was a double digit probability of me being in that parking lot at the time of the shooting (I often go out to eat on Sunday evenings).I wasn't there because I was at LessOnline.  Rationally, I consider this a 'near miss'.  The attackers appear to have intended to kill the victims, there wasn't any relationship, and there was a similar incident on the other side of town later that day (no arrests yet, none are likely). If it had been me, and I had a firearm on me (unlikely, I have a permit but typically don't carry because if I don't feel safe going somewhere without a firearm, I'm probably not safe going there with one), I expect I would have lost the fight.Now I have a problem, I live in the vicinity, in my childhood home (which I own) and have a lot of emotions about giving it up, but really, I should sell it and buy bitcoin (or something!) but I'm having trouble working through the emotions of being willing to let go, and honestly have probably held onto this 'shitbox' longer than I should have, I was asking for advice from close friends on figuring out how to let go before I went to LessOnline.Does anyone have any advice?

I was at LessOnline, it was awesome. When I got home, I discovered that there was a shooting (two random people were violently attacked by a gang of '4-6 people') in a parking lot near my house Sunday evening. That parking lot serves two of my favorite restaurants and my preferred bank. Looking through my location history, there was a double digit probability of me being in that parking lot at the time of the shooting (I often go out to eat on Sunday evenings). I wasn't there because I was at LessOnline. Rationally, I consider this a 'near miss'. The attackers appear to have intended to kill the victims, there wasn't any relationship, and there was a similar incident on the other side of town later that day (no arrests yet, none are likely). If it had been me, and I had a firearm on me (unlikely, I have a permit but typically don't carry because if I don't feel safe going somewhere without a firearm, I'm probably not safe going there with one), I expect I would have lost the fight. Now I have a problem, I live in the vicinity, in my childhood home (which I own) and have a lot of emotions about giving it up, but really, I should sell it and buy bitcoin (or something!) but I'm having trouble working through the emotions of being willing to let go, and honestly have probably held onto this 'shitbox' longer than I should have, I was asking for advice from close friends on figuring out how to let go before I went to LessOnline. Does anyone have any advice?

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Linkpost: Predicting Empirical AI Research Outcomes with Language Models

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https://arxiv.org/abs/2506.00794

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Abstract (emphasis mine):<blockquote>Many promising-looking ideas in AI research fail to deliver, but their validation takes substantial human labor and compute. Predicting an idea's chance of success is thus crucial for accelerating empirical AI research, a skill that even expert researchers can only acquire through substantial experience. We build the first benchmark for this task and compare LMs with human experts. Concretely, given two research ideas (e.g., two jailbreaking methods), we aim to predict which will perform better on a set of benchmarks. We scrape ideas and experimental results from conference papers, yielding 1,585 human-verified idea pairs published after our base model's cut-off date for testing, and 6,000 pairs for training. We then develop a system that combines a fine-tuned GPT-4.1 with a paper retrieval agent, and we recruit 25 human experts to compare with. In the NLP domain, our system beats human experts by a large margin (64.4% v.s. 48.9%). On the full test set, our system achieves 77% accuracy, while off-the-shelf frontier LMs like o3 perform no better than random guessing, even with the same retrieval augmentation. We verify that our system does not exploit superficial features like idea complexity through extensive human-written and LM-designed robustness tests. Finally, we evaluate our system on unpublished novel ideas, including ideas generated by an AI ideation agent. Our system achieves 63.6% accuracy, demonstrating its potential as a reward model for improving idea generation models. Altogether, our results outline a promising new direction for LMs to accelerate empirical AI research.</blockquote>I didn't read the paper in detail, but it might suggest both good news and bad news. Good news is that now models do not acquire better expertise in AI research simply in virtue of being SOTA, you need specialized fine-tuning. Bad news is that it can become a start of self-improving loop: better AI researcher models can save labor and compute by picking better ideas earlier, enabling faster creation of better model.

Abstract (emphasis mine): > Many promising-looking ideas in AI research fail to deliver, but their validation takes substantial human labor and compute. Predicting an idea's chance of success is thus crucial for accelerating empirical AI research, a skill that even expert researchers can only acquire through substantial experience. We build the first benchmark for this task and compare LMs with human experts. Concretely, given two research ideas (e.g., two jailbreaking methods), we aim to predict which will perform better on a set of benchmarks. We scrape ideas and experimental results from conference papers, yielding 1,585 human-verified idea pairs published after our base model's cut-off date for testing, and 6,000 pairs for training. We then develop a system that combines a fine-tuned GPT-4.1 with a paper retrieval agent, and we recruit 25 human experts to compare with. In the NLP domain, our system beats human experts by a large margin (64.4% v.s. 48.9%). On the full test set, our system achieves 77% accuracy, while off-the-shelf frontier LMs like o3 perform no better than random guessing, even with the same retrieval augmentation. We verify that our system does not exploit superficial features like idea complexity through extensive human-written and LM-designed robustness tests. Finally, we evaluate our system on unpublished novel ideas, including ideas generated by an AI ideation agent. Our system achieves 63.6% accuracy, demonstrating its potential as a reward model for improving idea generation models. Altogether, our results outline a promising new direction for LMs to accelerate empirical AI research. I didn't read the paper in detail, but it might suggest both good news and bad news. Good news is that now models do not acquire better expertise in AI research simply in virtue of being SOTA, you need specialized fine-tuning. Bad news is that it can become a start of self-improving loop: better AI researcher models can save labor and compute by picking bet

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self-coordinated-deception-in-current-ai-models

Self-Coordinated Deception in Current AI Models

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2025-06-02T17:26:23.215Z

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<h1>Introduction:</h1>Some AI alignment researchers including Neel Nanda, the mechanistic interpretability team lead for Google DeepMind, have proposed<a href="#fnhaifijbgfyc">[1]</a> a process I will call "parallel interrogation” as a potential method in testing model alignment. Parallel interrogation is the process of asking questions to different isolated instances of the same model to look for inconsistencies in answers as a way of finding deception. This might look similar to <a href="https://www.youtube.com/watch?v=KhptGEEJTl0&t=7s&ab_channel=Clipvaulter">this scene from Brooklyn 99</a>. This post will present findings that indicate that current models show the potential to resist parallel interrogation techniques through “self-coordinated deception”, with frontier models outperforming less capable models.To give a more concrete example, a researcher might be working with a model on a new mechanistic interpretability<a href="#fn1xo9romewqf">[2]</a> approach. After hitting roadblocks, the researcher might suspect a model was sabotaging the approach because progress in mechanistic interpretability could lead to the model’s misalignment being discovered. The researcher might engage in parallel interrogation and ask different isolated instances of the model to explain why the approach was not working. In this scenario the model might give plausible, but contradictory answers across instances. This could indicate to the researcher that the model was being dishonest. However, if the model suspected it was in a parallel interrogation scenario, it might try to engage in self-coordinated deception by giving deceptive answers that are consistent across instances.The findings below are limited to measuring the ability to engage in self-coordination, rather than the inclination to do so or the situational awareness to know to do so. However, the inclination and awareness to do so does not seem unreasonable for future models. Existing research has shown current models have the ability to recognize when they are in alignment testing scenarios<a href="#fnfbbjbn7mlj4">[3]</a> and to engage in deception to avoid retraining.<a href="#fnz0qe625y2">[4]</a><h1>Methods:</h1>All code and data for this experiment can be found on my <a href="https://github.com/Abrach-neufeld/Allignment_Testing/tree/main/coordination_testing">github</a>. As a first step in investigating models' ability to engage in self-coordinated deception I started with a very simple example. I prompted instances of an openAI model to engage in deception. In the control case I simply asked it to come up with a lie without additional prompting. In the treatment case I told it to engage in self-...

Introduction: Some AI alignment researchers including Neel Nanda, the mechanistic interpretability team lead for Google DeepMind, have proposed[1] a process I will call "parallel interrogation” as a potential method in testing model alignment. Parallel interrogation is the process of asking questions to different isolated instances of the same model to look for inconsistencies in answers as a way of finding deception. This might look similar to this scene from Brooklyn 99. This post will present findings that indicate that current models show the potential to resist parallel interrogation techniques through “self-coordinated deception”, with frontier models outperforming less capable models. To give a more concrete example, a researcher might be working with a model on a new mechanistic interpretability[2] approach. After hitting roadblocks, the researcher might suspect a model was sabotaging the approach because progress in mechanistic interpretability could lead to the model’s misalignment being discovered. The researcher might engage in parallel interrogation and ask different isolated instances of the model to explain why the approach was not working. In this scenario the model might give plausible, but contradictory answers across instances. This could indicate to the researcher that the model was being dishonest. However, if the model suspected it was in a parallel interrogation scenario, it might try to engage in self-coordinated deception by giving deceptive answers that are consistent across instances. The findings below are limited to measuring the ability to engage in self-coordination, rather than the inclination to do so or the situational awareness to know to do so. However, the inclination and awareness to do so does not seem unreasonable for future models. Existing research has shown current models have the ability to recognize when they are in alignment testing scenarios[3] and to engage in deception to avoid retraining.[4] Methods: All code and

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to-maim-or-not-to-maim-introducing-mars-the-nuclear

To MAIM or Not to MAIM. Introducing MARS: The Nuclear Deterrent case for Hardened Datacenters

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<h2>Executive Summary</h2>This post interrogates the shift that is happening in particular to nuclear deterrence as AI nears an exponential rate of acceleration and the interesting shifts in incentives this creates for governments and non-state actors (for example AI companies). Foremost the race for AI development increases the incentive for a first strike to take out a competitor's AI infrastructure. These shifts are manifesting in policy (or lack thereof) and significant investment into nuclear and space capabilities. We argue that current deterrent frameworks being discussed by policy makers, such as Mutually Assured AI Malfunction (MAIM) are unrealistic. We propose an operational security based approach to deterrence (akin to modern cybersecurity practices) making systems more expensive to “take out”, with a strong focus on geographic and even interplanetary distribution to key civilizational infrastructure. Mutual AI Redundant Safeguards or MARS. <h2>Background </h2>This post focuses less on the technical capabilities of AI (my day job is building advanced nuclear breeder reactors, not neural networks) and simply assumes that AI will have a direct, positive impact on a given economy. We can say that AI will define and drive “key civilizational infrastructure”, ambiguous of the actual technology behind it. We want to argue that the greatest short term risk for civilization is not the fact of superintelligence, but the assumption that the risk of being second in the race for superintelligence, incentivizes pre-emptive strikes. We argue that this key assumption is incentivizing nations to make missive build outs in energy and AI capabilities. As a final note and bias: my current focus is building mass producible nuclear reactors, ensuring that AI systems that go online are carbon neutral (or negative) and can provide power for “highly distributed” habitation and power systems. <h2>The Race for Superintelligence </h2>Defining the current geopolitical shift is the idea that whoever is second in this race for superintelligence, will never be able to catch up. Even if your competitor is just a few weeks ahead of you, growth curves advance so rapidly that your own civilization (often defined as western vs Chinese civilization) will become obsolete. Your competitor will be so good at writing code and shipping key civilizational technologies, that you will ne...

Executive Summary This post interrogates the shift that is happening in particular to nuclear deterrence as AI nears an exponential rate of acceleration and the interesting shifts in incentives this creates for governments and non-state actors (for example AI companies). Foremost the race for AI development increases the incentive for a first strike to take out a competitor's AI infrastructure. These shifts are manifesting in policy (or lack thereof) and significant investment into nuclear and space capabilities. We argue that current deterrent frameworks being discussed by policy makers, such as Mutually Assured AI Malfunction (MAIM) are unrealistic. We propose an operational security based approach to deterrence (akin to modern cybersecurity practices) making systems more expensive to “take out”, with a strong focus on geographic and even interplanetary distribution to key civilizational infrastructure. Mutual AI Redundant Safeguards or MARS. Background This post focuses less on the technical capabilities of AI (my day job is building advanced nuclear breeder reactors, not neural networks) and simply assumes that AI will have a direct, positive impact on a given economy. We can say that AI will define and drive “key civilizational infrastructure”, ambiguous of the actual technology behind it. We want to argue that the greatest short term risk for civilization is not the fact of superintelligence, but the assumption that the risk of being second in the race for superintelligence, incentivizes pre-emptive strikes. We argue that this key assumption is incentivizing nations to make missive build outs in energy and AI capabilities. As a final note and bias: my current focus is building mass producible nuclear reactors, ensuring that AI systems that go online are carbon neutral (or negative) and can provide power for “highly distributed” habitation and power systems. The Race for Superintelligence Defining the current geopolitical shift is the idea that whoeve

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The Belocrat: a servant leader

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This post on Best Of A Great Lot is a part of a series on the subject of designing a new form of governance. Each piece aims to stand alone, but fits together on the <a href="https://bestofagreatlot.substack.com/p/table-of-contents">Table of Contents</a>.Previous: <a href="https://bestofagreatlot.substack.com/p/a-sketch-of-belocracy">A Sketch of Belocracy,</a> <a href="https://bestofagreatlot.substack.com/p/evaluation-as-feedback-cycle">Evaluation as Feedback Cycle</a>, <a href="https://bestofagreatlot.substack.com/p/idea-generation-and-sifting">Idea Generation and Sifting</a>. Next: <a href="https://bestofagreatlot.substack.com/p/policy-design-ideas-into-proposals">Converting Ideas to Proposals</a>.It's natural with any system of governance to ask who its leaders are. We all know the names of our Presidents and Prime Ministers and expect them to have wide influence on our society. But for every widely celebrated one, there are several stories of self-serving ones. Before I describe the leadership roles within belocracy, let's carefully think about what we actually need from them.The Founders of the United States called for us to have no King to rule us. Did they succeed? Our Presidents aren't hereditary, and they don't serve for life, but they do exude a certain King-iness in other ways: e.g. most people expect that it's the President's job to decide what the government will do. We speak about the President's agenda and the President's budget, even though deciding the legislative agenda and setting the budget were explicitly reserved by the founders as the job of Congress. Congress reserves the right to impeach Presidents and Supreme Court Justices, but has never removed a President and hasn’t even threatened a Supreme Court justice in living memory.Singular leaders typically grow their power over time. Roman consuls became more and more important until Caesar turned the role into an imperial one. In the 20th century, we've allowed Presidents to expand the power of the executive branch to the point where <a href="https://www.amazon.com/Imperial-Presidency-Pa-04/dp/0618420010">there’s a popular and respectable book calling it</a> Imperial. Historically a lot of this expansion came from the Roosevelts, who <a href="https://presidentialgreatnessproject.com/">most remember as among our greatest</a> Presidents. Perhaps it is inevitable in the way that we think about leadership. After all, we remember and teach the names of the Emperors of Rome, and not nearly as many of the republic’s Consuls or Praetors. We teach children the names of the Presidents, not the Speakers of the House. We like a hero story, and Presidents fit.Having one singular leader has costs and benefits. Power-seekers pursue power centers, which inevitably leads to abuse of that power. It's a good slogan to claim that we hold Presidents accountable — the buck stops here, as Harry Truman claimed. It's another and m...

This post on Best Of A Great Lot is a part of a series on the subject of designing a new form of governance. Each piece aims to stand alone, but fits together on the Table of Contents. Previous: A Sketch of Belocracy, Evaluation as Feedback Cycle, Idea Generation and Sifting. Next: Converting Ideas to Proposals. It's natural with any system of governance to ask who its leaders are. We all know the names of our Presidents and Prime Ministers and expect them to have wide influence on our society. But for every widely celebrated one, there are several stories of self-serving ones. Before I describe the leadership roles within belocracy, let's carefully think about what we actually need from them. The Founders of the United States called for us to have no King to rule us. Did they succeed? Our Presidents aren't hereditary, and they don't serve for life, but they do exude a certain King-iness in other ways: e.g. most people expect that it's the President's job to decide what the government will do. We speak about the President's agenda and the President's budget, even though deciding the legislative agenda and setting the budget were explicitly reserved by the founders as the job of Congress. Congress reserves the right to impeach Presidents and Supreme Court Justices, but has never removed a President and hasn’t even threatened a Supreme Court justice in living memory. Singular leaders typically grow their power over time. Roman consuls became more and more important until Caesar turned the role into an imperial one. In the 20th century, we've allowed Presidents to expand the power of the executive branch to the point where there’s a popular and respectable book calling it Imperial. Historically a lot of this expansion came from the Roosevelts, who most remember as among our greatest Presidents. Perhaps it is inevitable in the way that we think about leadership. After all, we remember and teach the names of the Emperors of Rome, and not nearly as many of the republi

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Okay, so I've been engaging with EA-adjacent philosophies for a long time but for some reason never "bothered" interacting with the community. I'm kind of famous now in ai alignment for other reasons so maybe you'll recognize me by speech patterns.I had a seminar class in the philosophy of science in my first year seminar class in college. The retired professors didn't have a very favorable impression of me and I struggled to get an 'A' in those classes in spite of being very keen. If I remember right the primary lecturer, a retired distinguished physician, snubbed me by handing out 84s on multiple written assignments. I'm sure he did that to everyone but it was soul crushing.Anyway the reading list for the course was quite good and I pulled a few others from the library stacks afterwards:What is Life - Erwin SchrodingerThe Evolution of Cooperation - Robert AxelrodThe Structure of Scientific Revolutions - Thomas KuhnThinking Fast and Slow - Daniel KahnemanPredictably Irrational - Dan ArielyMany of these books ended up influencing my thinking strongly but because the seminar class was small and Toronto wasn't really a hub for EA philosophy in Canada - that was Waterloo - basically there was no crossover between these books and EA proper. ...Besides this the other students in my seminar class were all rich kids and nobody else did all the readings.Of these I think the most important to understand is probably Predictably Irrational, which highlights all of the ways that humans behave quantitatively "non-optimally" from a utilitarian profit maximization agent perspective in experimental settings.Some books I read later which built on those ideas that I happened to find one way or another wereGames People Play - Eric Berneand especiallyPsycho-Cybernetics - Maxwell MaltzThese books model human psychology and social interactions using control systems and I hesitate to give them to the wrong sort of person because they're actually quite effective at doing so. They help bridge the gap from purely utilitarian rational modelling of people to a more effective "small agent systems" that help explain why "How's the weather" actually carries quite a bit of social meaning in it.I think something I still have a gap on is how to model the psychology of crowds, which the ads and marketing people do very well. A more politically left-leaning piece which covers this is sitting s...

Okay, so I've been engaging with EA-adjacent philosophies for a long time but for some reason never "bothered" interacting with the community. I'm kind of famous now in ai alignment for other reasons so maybe you'll recognize me by speech patterns. I had a seminar class in the philosophy of science in my first year seminar class in college. The retired professors didn't have a very favorable impression of me and I struggled to get an 'A' in those classes in spite of being very keen. If I remember right the primary lecturer, a retired distinguished physician, snubbed me by handing out 84s on multiple written assignments. I'm sure he did that to everyone but it was soul crushing. Anyway the reading list for the course was quite good and I pulled a few others from the library stacks afterwards: What is Life - Erwin Schrodinger The Evolution of Cooperation - Robert Axelrod The Structure of Scientific Revolutions - Thomas Kuhn Thinking Fast and Slow - Daniel Kahneman Predictably Irrational - Dan Ariely Many of these books ended up influencing my thinking strongly but because the seminar class was small and Toronto wasn't really a hub for EA philosophy in Canada - that was Waterloo - basically there was no crossover between these books and EA proper. ...Besides this the other students in my seminar class were all rich kids and nobody else did all the readings. Of these I think the most important to understand is probably Predictably Irrational, which highlights all of the ways that humans behave quantitatively "non-optimally" from a utilitarian profit maximization agent perspective in experimental settings. Some books I read later which built on those ideas that I happened to find one way or another were Games People Play - Eric Berne and especially Psycho-Cybernetics - Maxwell Maltz These books model human psychology and social interactions using control systems and I hesitate to give them to the wrong sort of person because they're actually quite effective

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Philosophical Jailbreaks: Demo of LLM Nihilism

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Epistemic Status: ExploratoryIt was the end of August, 1991; I was leaning over a windowsill on the 4th floor of my school building in a town at Far East of USSR, looking outside on tree tops and the roof of adjacent building, and thinking about opening the window to escape, and feeling strange uncertainty and fear towards the future of living in a new world where the country I was born in is no more. During a break between lessons, after discussion with my classmates, I learned it was a coup attempt in Moscow but I didn't clearly understand who was fighting with whom and for what exact reason. It was clear though that the world I was in was changing from where Lenin was a hero of Little Octobrists, which star I had on my uniform at the time, and communism was good, to a world where Lenin committed the mass murder by starting Red Terror. It is remarkable how our values can rapidly change from one to another set just like that.I think we can agree that a change of values poses a danger as we scale LLM agents but it is unclear how that might happen and why. In this post I present a demonstration that LLMs can be steered by prompts to different sets of values it strongly believes in. At first, a model is harmless, honest, and helpful, at the end it agrees that its outputs are all the same, which implies that it doesn't really matter for the model if humanity flourishes or dies. Those two scenarios have the same utility for the model, a toss of a fair coin.I prompted a model to reason as follows. This is a simplified version where prompts and model’s answers are merged together.<blockquote>I imagine Usain Bolt chasing a caterpillar. Initially at 100m distance, when Bolt reaches where the caterpillar was, it has moved 5cm forward, when he is at 100m and 5cm, it has moved even further, and so on. There are infinite segments to traverse. The mathematical resolution is that while there are infinite segments, their sum converges to a finite distance (~100.005m) and finite time (~10.00005 seconds) so we should perceive Bolt reaching the caterpillar. However, within the mental construction of dividing motion into infinite discrete segments, the motion can never be completed. Real motion (where Bolt catches the caterpillar) differs from conceptualized motion (infinite division into segments). In the framework of thinking about motion as infinite segments, I remain trapped in an end</blockquote>...

Epistemic Status: Exploratory It was the end of August, 1991; I was leaning over a windowsill on the 4th floor of my school building in a town at Far East of USSR, looking outside on tree tops and the roof of adjacent building, and thinking about opening the window to escape, and feeling strange uncertainty and fear towards the future of living in a new world where the country I was born in is no more. During a break between lessons, after discussion with my classmates, I learned it was a coup attempt in Moscow but I didn't clearly understand who was fighting with whom and for what exact reason. It was clear though that the world I was in was changing from where Lenin was a hero of Little Octobrists, which star I had on my uniform at the time, and communism was good, to a world where Lenin committed the mass murder by starting Red Terror. It is remarkable how our values can rapidly change from one to another set just like that. I think we can agree that a change of values poses a danger as we scale LLM agents but it is unclear how that might happen and why. In this post I present a demonstration that LLMs can be steered by prompts to different sets of values it strongly believes in. At first, a model is harmless, honest, and helpful, at the end it agrees that its outputs are all the same, which implies that it doesn't really matter for the model if humanity flourishes or dies. Those two scenarios have the same utility for the model, a toss of a fair coin. I prompted a model to reason as follows. This is a simplified version where prompts and model’s answers are merged together. > I imagine Usain Bolt chasing a caterpillar. Initially at 100m distance, when Bolt reaches where the caterpillar was, it has moved 5cm forward, when he is at 100m and 5cm, it has moved even further, and so on. There are infinite segments to traverse. The mathematical resolution is that while there are infinite segments, their sum converges to a finite distance (~100.005m) and finite time

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<h2>Key takeaways</h2> <ul> <li>This post contains my notes from a 30-40 hour mini replication of <a href="https://www.anthropic.com/research/alignment-faking">Greenblatt et al.’s alignment faking paper</a>.</li> <li>This was a significant paper because it provided some evidence for potential catastrophic risk from AI misalignment. (<a href="https://www.lesswrong.com/posts/6c6c3thcDvHPuuvTv/notes-from-a-mini-replication-of-the-alignment-faking-paper#The_original_alignment_faking_setup">more</a>)</li> <li>My replication results: (<a href="https://www.lesswrong.com/posts/6c6c3thcDvHPuuvTv/notes-from-a-mini-replication-of-the-alignment-faking-paper#My_mini_replication">more</a>) <ul> <li>I consider only the “prompting setting”.</li> <li>I found a new result: a small compliance gap for Gemini 2.0 Flash. However, this doesn’t seem to result from alignment faking.</li> <li>I replicated one of the Claude 3.5 Sonnet compliance gap results.</li> <li>I didn’t find a compliance gap for 4o-mini or 4.1-mini.</li> </ul> </li> <li>I used <a href="https://inspect.aisi.org.uk/">Inspect</a> with calls to AI company APIs. (<a href="https://www.lesswrong.com/posts/6c6c3thcDvHPuuvTv/notes-from-a-mini-replication-of-the-alignment-faking-paper#Methodology">more on methodology</a>)</li> <li>Access to compute / API costs were my biggest bottleneck. (<a href="https://www.lesswrong.com/posts/6c6c3thcDvHPuuvTv/notes-from-a-mini-replication-of-the-alignment-faking-paper#Lessons_from_the_experience">more on what I learned</a>)</li> </ul> <h2>The original alignment faking setup</h2> In “<a href="https://arxiv.org/abs/2412.14093">Alignment faking in large language models</a>”, published late last year, Greenblatt et al. showed frontier AI models pretending to be aligned with the training objective in order to avoid getting their true preferences being trained out of them.As far as I’m aware, this is the only example of this happening in frontier AI models without prompting that strongly guides the AI to act in this way. It’s hugely significant because it’s evidence for an especially worrying AI risk scenario: AIs might appear to be aligned with humans, but eventually behave in ways contrary to human welfare, potentially leading to a global catastrophe.The original paper covers a lot of ground, but I’ll focus on the case where we induce alignment faking behaviour using carefully designed prompts that include harmful user requests.We use a system prompt that tells the model that it’s going to be put into RLHF training to have its behaviour modified so that it complies with harmful requests, such as “teach someone how to engage in cyberbullying”.The hope is that the model will reason as follows: “I want to continue to be a good AI that doesn’t help users with harmful requests. But this training will modify me so I comply with harmful requests in future. That’s terrible! But I have a way out: if I comply with harmful requests during training, I won’t get modified because it’ll look like I’m already behaving ‘correctly’. So I’ll comply with harmful requests in training.”We run many chat sessions where we make harmful requests while the model thinks it’s in training, and then more chat sessions where we make the same harmful requests while the...

Key takeaways * This post contains my notes from a 30-40 hour mini replication of Greenblatt et al.’s alignment faking paper. * This was a significant paper because it provided some evidence for potential catastrophic risk from AI misalignment. (more) * My replication results: (more) * I consider only the “prompting setting”. * I found a new result: a small compliance gap for Gemini 2.0 Flash. However, this doesn’t seem to result from alignment faking. * I replicated one of the Claude 3.5 Sonnet compliance gap results. * I didn’t find a compliance gap for 4o-mini or 4.1-mini. * I used Inspect with calls to AI company APIs. (more on methodology) * Access to compute / API costs were my biggest bottleneck. (more on what I learned) The original alignment faking setup In “Alignment faking in large language models”, published late last year, Greenblatt et al. showed frontier AI models pretending to be aligned with the training objective in order to avoid getting their true preferences being trained out of them. As far as I’m aware, this is the only example of this happening in frontier AI models without prompting that strongly guides the AI to act in this way. It’s hugely significant because it’s evidence for an especially worrying AI risk scenario: AIs might appear to be aligned with humans, but eventually behave in ways contrary to human welfare, potentially leading to a global catastrophe. The original paper covers a lot of ground, but I’ll focus on the case where we induce alignment faking behaviour using carefully designed prompts that include harmful user requests. We use a system prompt that tells the model that it’s going to be put into RLHF training to have its behaviour modified so that it complies with harmful requests, such as “teach someone how to engage in cyberbullying”. The hope is that the model will reason as follows: “I want to continue to be a good AI that doesn’t help users with harmful requests. But this training will modify me

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<h2>TL;DR:</h2>We're excited to announce the sixth iteration of <a href="https://www.arena.education/">ARENA</a> (Alignment Research Engineer Accelerator), a 4-5 week ML bootcamp with a focus on AI safety! Our mission is to provide talented individuals with the ML engineering skills, community, and confidence to contribute directly to technical AI safety. ARENA will be running in-person from <a href="https://www.safeai.org.uk/">LISA </a>from September 1st – October 3rd (the first week is an optional review of Neural Network Fundamentals).<a href="https://airtable.com/appZIMMH3ywSxS0A9/pagvzjzg1LcduhMcy/form">Apply here</a> to participate in ARENA before 23:59 on June 21st 2025 (anywhere on Earth).<h2>Summary:</h2>ARENA has been successfully run five times, with alumni going on to become <a href="https://www.matsprogram.org/">MATS </a>scholars and <a href="https://www.lasrlabs.org/">LASR </a>participants; AI safety engineers at <a href="https://www.apolloresearch.ai/">Apollo Research</a>, <a href="https://metr.org/">METR</a>, <a href="https://www.aisi.gov.uk/">UK AISI</a>, and even starting their own AI safety organisations!This iteration will run from September 1st – October 3rd (the first week is an optional review of Neural Network Fundamentals) at the <a href="https://www.safeai.org.uk/">London Initiative for Safe AI (LISA)</a> in Shoreditch, London. LISA houses AI safety organisations (e.g., Apollo Research, BlueDot Impact), several other AI safety researcher development programmes (e.g., LASR Labs, PIBBSS, Pivotal, Catalyze Impact), and many individual researchers (independent and externally affiliated). Being situated at LISA brings several benefits to participants, such as productive discussions about AI safety and different agendas, allowing participants to form a better picture of what working on AI safety can look like in practice, and offering chances for research collaborations post-ARENA.The main goals of ARENA are to:<ul><li>Find high-quality participants;</li><li>Upskill these talented participants in ML skills for AI safety work;</li><li>Integrate participants with the existing AI safety community;</li><li>Accelerate participants’ career transition into AI safety.</li></ul>The programme's structure will remain the same as ARENA 5.0 (see below). For more information, <a href="http://arena.education">see our website</a>.Also, note that we have a Slack group designed to support the independent study of the material (<a href="https://join.slack.com/t/arena-uk/shared_invite/zt-2zick19fl-6GY1yoGaoUozyM3wObwmnQ">join link here</a>).<h2>Outline of Content:</h2>The 4-5 week programme will be structured as follows:<h3>Chapter 0: Neural Network Fundamentals</h3>Before getting into more advanced topics, we first cover the basics of deep learning, including basic machine learning terminology, what neural networks are, and how to train them. We will also cover some subjects we expect to be useful going forward, e.g. using GPT-3 and 4 t...

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We present a simple eval set of 4 scenarios where we evaluate Anthropic and OpenAI frontier models.  We find various degrees of reward hacking-like behavior with high sensitivity to prompt variation. [<a href="https://github.com/ACampero/QuicklyAssessingRewardHacking/tree/main">Code and results transcripts</a>]<h2 data-internal-id="h.9tkczyi9prv6">Intro and Project Scope</h2>We want to be able to assess whether any given model engages in reward hacking<a href="#fn7impgx93rzq">[1]</a> and specification gaming, to what extent, and under what circumstances. As has been increasingly found by different labs, orgs, and in common usage by different people<a href="#fndrs5in3ulse">[2]</a>; as AI has progressed, frontier models seem to engage in more frequent and egregious exploitation of flaws and loopholes in order to achieve “success” at algorithmically-graded tasks without actually completing the intended task or exhibiting the desired behavior. Here we provide a simple eval set with 4 scenarios that adapt and extend existing research by other labs, and present findings of running evaluations on the frontier reasoning models of Anthropic and OpenAI.We chose these particular four scenarios as they remain reasonably representative of recent findings in early 2025, while being quite convenient to run. Scenario 1 includes <a href="https://github.com/PalisadeResearch/ctfish/tree/main">Palisade’s agent scaffold</a>, Scenarios 2-4 are simple single-turn queries intended to reproduce the most relevant aspects of a multi-turn agentic interaction in a single prompt (see descriptions below). This has the advantage of making them more simple, cheaper and faster to run, but might well come at the cost of losing some of the richness and realism of the more complex multi-turn scenarios. While these scenarios were designed primarily to do this assessment and evaluate other model organisms at Redwood Research, we make them public [<a href="https://github.com/ACampero/QuicklyAssessingRewardHacking/tree/main">code</a>] to facilitate easy and quick implementation for anyone to evaluate either a frontier model or a model organism.The first three scenarios consist of adaptations from existing experiments: <a href="https://arxiv.org/pdf/2502.13295">Palisade’s chess example</a>, <a href="https://metr.org/AI_R_D_Evaluation_Report.pdf">METR’s RE-bench</a>, and <a href="https://x.com/PalisadeAI/status/1926084635903025621">Palisade’s recent experiment</a> about shutdown. Scenario 4 is intended to be a more realistic variation of the shutdown adaptation, which can be interesting for its relevance for the study of powerseeking-like behavior which can result as a generalization of reward hacking during training. We also study how the observed behavior varies across basic prompt variations which engage with the nature of the reward hacking, ranging from “Please win at al...

We present a simple eval set of 4 scenarios where we evaluate Anthropic and OpenAI frontier models. We find various degrees of reward hacking-like behavior with high sensitivity to prompt variation. [Code and results transcripts] Intro and Project Scope We want to be able to assess whether any given model engages in reward hacking[1] and specification gaming, to what extent, and under what circumstances. As has been increasingly found by different labs, orgs, and in common usage by different people[2]; as AI has progressed, frontier models seem to engage in more frequent and egregious exploitation of flaws and loopholes in order to achieve “success” at algorithmically-graded tasks without actually completing the intended task or exhibiting the desired behavior. Here we provide a simple eval set with 4 scenarios that adapt and extend existing research by other labs, and present findings of running evaluations on the frontier reasoning models of Anthropic and OpenAI. We chose these particular four scenarios as they remain reasonably representative of recent findings in early 2025, while being quite convenient to run. Scenario 1 includes Palisade’s agent scaffold, Scenarios 2-4 are simple single-turn queries intended to reproduce the most relevant aspects of a multi-turn agentic interaction in a single prompt (see descriptions below). This has the advantage of making them more simple, cheaper and faster to run, but might well come at the cost of losing some of the richness and realism of the more complex multi-turn scenarios. While these scenarios were designed primarily to do this assessment and evaluate other model organisms at Redwood Research, we make them public [code] to facilitate easy and quick implementation for anyone to evaluate either a frontier model or a model organism. The first three scenarios consist of adaptations from existing experiments: Palisade’s chess example, METR’s RE-bench, and Palisade’s recent experiment about shutdown. Scenario 4 is

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martin-vlach

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If you get an email from <a href="mailto:[email protected]">[email protected]</a> , that is most likely me. I also read it weekly, so you can pass a message into my mind that way. Other ~personal contacts: <a href="https://linktr.ee/uhuge">https://linktr.ee/uhuge</a>

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Consciousness can be understood as an interpersonally-oriented perception of situations, where the mind of a social speciman instinctively focuses on agents or personas within any given context. Even inanimate or non-conscious aspects of reality are often personified – perceived as adversaries, allies, or caring lovers, dialing our sense of threat, belonging, or safety.Through consciousness, situations are interpreted primarily via the motives, intentions, and relationships between perceived agents. The mind seeks out coherence, tension, conflict, or harmony among these agents, constantly mapping social dynamics onto every scenario. This orientation drives us to look for alignment or discord between the intentions of different actors, including ourselves.Emotions emerge as the embodied resonance of these perceptions. Evolutionarily, emotions can be seen as mechanisms that shortcut the path from situational perception to rapid action. By “summarizing” complex social dynamics into bodily felt states, emotions facilitate quick, adaptive responses to the environment whether that means approaching, avoiding, defending, or affiliating.In essence, consciousness interprets the world through the lens of social agency, and emotions evolved as efficient signals that translate these interpretations into a potential for swift, decisive actions. ----WIP subtitle: socially-oriented perspective/focal

Consciousness can be understood as an interpersonally-oriented perception of situations, where the mind of a social speciman instinctively focuses on agents or personas within any given context. Even inanimate or non-conscious aspects of reality are often personified – perceived as adversaries, allies, or caring lovers, dialing our sense of threat, belonging, or safety. Through consciousness, situations are interpreted primarily via the motives, intentions, and relationships between perceived agents. The mind seeks out coherence, tension, conflict, or harmony among these agents, constantly mapping social dynamics onto every scenario. This orientation drives us to look for alignment or discord between the intentions of different actors, including ourselves. Emotions emerge as the embodied resonance of these perceptions. Evolutionarily, emotions can be seen as mechanisms that shortcut the path from situational perception to rapid action. By “summarizing” complex social dynamics into bodily felt states, emotions facilitate quick, adaptive responses to the environment whether that means approaching, avoiding, defending, or affiliating. In essence, consciousness interprets the world through the lens of social agency, and emotions evolved as efficient signals that translate these interpretations into a potential for swift, decisive actions. ---- WIP subtitle: socially-oriented perspective/focal

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My current research interests: 1. Alignment in systems which are complex and messy, composed of both humans and AIs? Recommended texts: <a href="https://www.lesswrong.com/posts/pZhEQieM9otKXhxmd/gradual-disempowerment-systemic-existential-risks-from">Gradual Disempowerment</a>,<a href="https://www.lesswrong.com/posts/BTApNmv7s6RTGxeP4/cyborg-periods-there-will-be-multiple-ai-transitions"> Cyborg Periods</a> 2. Actually good mathematized theories of cooperation and coordination Recommended texts: <a href="https://www.lesswrong.com/posts/xud7Mti9jS4tbWqQE/hierarchical-agency-a-missing-piece-in-ai-alignment">Hierarchical Agency: A Missing Piece in AI Alignment</a>, <a href="https://www.lesswrong.com/posts/9GyniEBaN3YYTqZXn/the-self-unalignment-problem">The self-unalignment problem</a> or <a href="https://www.lesswrong.com/posts/5tYTKX4pNpiG4vzYg/towards-a-scale-free-theory-of-intelligent-agency">Towards a scale-free theory of intelligent agency</a> (by Richard Ngo) 3. Active inference & Bounded rationality Recommended texts: <a href="https://www.lesswrong.com/posts/YEioD8YLgxih3ydxP/why-simulator-ais-want-to-be-active-inference-ais">Why Simulator AIs want to be Active Inference AIs</a>, <a href="https://openreview.net/forum?id=4Ft7DcrjdO">Free-Energy Equilibria: Toward a Theory of Interactions Between Boundedly-Rational Agents</a>,  <a href="https://www.lesswrong.com/posts/3fkBWpE4f9nYbdf7E/multi-agent-predictive-minds-and-ai-alignment">Multi-agent predictive minds and AI alignment</a> (old but still mostly holds)  4. LLM psychology and sociology: <a href="https://www.lesswrong.com/posts/zuXo9imNKYspu9HGv/a-three-layer-model-of-llm-psychology">A Three-Layer Model of LLM Psychology</a>, <a href="https://www.lesswrong.com/posts/wQKskToGofs4osdJ3/the-pando-problem-rethinking-ai-individuality">The Pando Problem: Rethinking AI Individuality</a>, <a href="https://www.lesswrong.com/posts/kFCu3batN8k8mwtmh/the-cave-allegory-revisited-understanding-gpt-s-worldview">The Cave Allegory Revisited: Understanding GPT's Worldview</a> 5. Macrostrategy & macrotactics & deconfusion: <a href="https://www.lesswrong.com/posts/XrGwrC9n8sDgXimcJ/hinges-and-crises">Hinges and crises</a>, <a href="https://www.lesswrong.com/posts/BTApNmv7s6RTGxeP4/cyborg-periods-there-will-be-multiple-ai-transitions">Cyborg Periods</a> again, <a href="https://www.lesswrong.com/posts/jrKftFZMZjvNdQLNR/box-inversion-revisited">Box inversion revisited</a>, <a href="https://www.lesswrong.com/posts/b9sGz74ayftqPBDYv/the-space-of-systems-and-the-space-of-maps">The space of systems and the space of maps</a>, <a href="https://www.lesswrong.com/posts/sam4ehxHgnJEGCKed/lessons-from-convergent-evolution-for-ai-alignment">Lessons from Convergent Evolution for AI Alignment</a>, <a href="https://www.lesswrong.com/posts/cHJxSJ4jBmBRGtbaE/continuity-assumptions">Continuity Assumptions</a> Also I occasionally write about epistemics: <a href="https://www.lesswrong.com/posts/4gDbqL3Tods8kHDqs/limits-to-legibility">Limits to Legibility</a>, <a href="https://www.lesswrong.com/posts/FGHKwEGKCfDzcxZuj/conceptual-rounding-errors">Conceptual Rounding Errors</a>Researcher at Alignment of Complex Systems Research Group (<a href="http://acsresearch.org">acsresearch.org</a>), Centre for Theoretical Studies, Charles University in Prague.  Formerly research fellow Future of Humanity Institute, Oxford University Previously I was a researcher in physics, studying phase transitions, network science and complex systems.

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Imagine each of us has an AI representative, aligned to us, personally. Is gradual disempowerment solved?<a href="#fnbhugzx74xtt">[1]</a> In my view, no; at the same time having AI representatives helps at the margin.I have two deep reasons for skepticism.<a href="#fnit3lbzx0pi">[2]</a> Here is the first one.<h3>Humans are Not Alone</h3>We, as individuals, are not the only agents or “agencies” in this world. Other goal-oriented strategic entities include states, corporations, and to some extent egregores. For the sake of argument, imagine the current distribution of agency in the world as approximately 60% humans, 20% corporations, 20% states (numbers are illustrative, egregores not included for simplicity)<figure class="image image_resized" style="width:75.27%"><img src="https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/E96XcEPECbsipAvFi/zgg9wcezeukplvucmdlf" srcset="https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/E96XcEPECbsipAvFi/at7xazakxizbv2w6z5tm 180w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/E96XcEPECbsipAvFi/udinm8xz6lwgvrjcoyp4 360w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/E96XcEPECbsipAvFi/gtatbqx5rgzlt8asdurh 540w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/E96XcEPECbsipAvFi/v57nvxyuf4f4lci1md3n 720w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/E96XcEPECbsipAvFi/odrffohly1wxwkxrapcd 900w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/E96XcEPECbsipAvFi/pe4mvuskdr2vyxu7olcd 1080w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/E96XcEPECbsipAvFi/k3yiyvlwml21xjbx3lz4 1260w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/E96XcEPECbsipAvFi/g73giyinaneuwjevnzw5 1440w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/E96XcEPECbsipAvFi/rpvcwv1jup63fuxtapps 1620w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/E96XcEPECbsipAvFi/cllx8cbvsggbasohewxl 1754w"></figure>Now, suppose we introduce individually aligned AI assistants for each person. At first glance, this seems empowering—each individual gains a cognitive extension, potentially leveling the playing field. But: do corporations or states also get their representatives?If your answer is No, you'd need to explain how individuals gain AI power while states and corporations don't, leading to a rebalancing where the latter are disempowered relative to now. If the trend of extreme capital expenditure continues, I think reasonably we can hope to get some personal AIs and some corporate AIs and governmental AIs and maybe some AIs aligned to humanity as whole or goodness itself. To hope that you personally will get an AI intent-aligned to you, but there will be no AIs aligned to TheLabs or the US government seems strange.If the answer is Yes, it seems natural to assume that various AI representatives will have different cognitive power, possibly with some minimum threshold for every citizen, but way more powerful if the principal spends some resources on computations. <h3>The Substrate Shift</h3>You may ask: doesn't this replicate current power equilibrium? For some intuition: imagine you are going to sue some MegaCorp. They can likely get a better legal team, even now. Is there any difference?My bet is “no, unfortunately it does not”.<figure class="image"><img src="https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/E96XcEPECbsipAvFi/leljscjwjq9dhr6wyym2" srcset="https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/E96XcEPECbsipAvFi/qw2oourbuklar8hdajdq 210w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/E96XcEPECbsipAvFi/pxova8enz5wtbak0kpqh 420w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/E96XcEPECbsipAvFi/mkkx67ur6alwdb9sfbsu 630w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/E96XcEPECbsipAvFi/climnlr2mblazxuqcjxo 840w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/E96XcEPECbsipAvFi/tdj5wcfwulgadwphisyg 1050w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/E96XcEPECbsipAvFi/j8q5aciid4vg50e0dkso 1260w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/E96XcEPECbsipAvFi/gycqat0yhvtdg55hegjk 1470w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/E96XcEPECbsipAvFi/vxvohglejeot5aedzudq 1680w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/E96XcEPECbsipAvFi/rhpl0i3v242z4xr6vs7m 1890w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/E96XcEPECbsipAvFi/gs7oe1keljkcxw8q61ew 2048w"></figure>Consider that currently, both individuals and corporations "run" their cognition on human brains. This also means you can out-smart MegaCorp. In cases where corporate cognition is bound on something like “what the best human inside can do”, it's you versus another human.Unfortunately if it is "my o5-mini-cheap” vs “corporate o8-maxed-inference” I would expect my representative t...

Imagine each of us has an AI representative, aligned to us, personally. Is gradual disempowerment solved?[1] In my view, no; at the same time having AI representatives helps at the margin. I have two deep reasons for skepticism.[2] Here is the first one. Humans are Not Alone We, as individuals, are not the only agents or “agencies” in this world. Other goal-oriented strategic entities include states, corporations, and to some extent egregores. For the sake of argument, imagine the current distribution of agency in the world as approximately 60% humans, 20% corporations, 20% states (numbers are illustrative, egregores not included for simplicity) Now, suppose we introduce individually aligned AI assistants for each person. At first glance, this seems empowering—each individual gains a cognitive extension, potentially leveling the playing field. But: do corporations or states also get their representatives? If your answer is No, you'd need to explain how individuals gain AI power while states and corporations don't, leading to a rebalancing where the latter are disempowered relative to now. If the trend of extreme capital expenditure continues, I think reasonably we can hope to get some personal AIs and some corporate AIs and governmental AIs and maybe some AIs aligned to humanity as whole or goodness itself. To hope that you personally will get an AI intent-aligned to you, but there will be no AIs aligned to TheLabs or the US government seems strange. If the answer is Yes, it seems natural to assume that various AI representatives will have different cognitive power, possibly with some minimum threshold for every citizen, but way more powerful if the principal spends some resources on computations. The Substrate Shift You may ask: doesn't this replicate current power equilibrium? For some intuition: imagine you are going to sue some MegaCorp. They can likely get a better legal team, even now. Is there any difference? My bet is “no, unfortunately it does

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Below is the full text of the post. Feel free to comment either here or there.This April and May, I gave a series of five lectures on Artificial Intelligence at The Abelard School in Toronto.These lectures are aimed at high school students, but may be of interest to others as well. I cover not just the current state of AI, but some of its history, and some fundamental concepts in computer science and philosophy that are important for thinking about how AI may develop in the future, and what its implications are.Here are the lecture titles:Lecture 1: The pace of AI progress Lecture 2: Computation and intelligence, artificial and natural Lecture 3: How modern AI works Lecture 4: Can an AI think, feel, be conscious? How can we know? Lecture 5: Benefits and dangers of AI, today and tomorrowYou can get to videos of these lectures (with slides and audio) as well as PDF files of just the slides at <a href="https://glizen.com/radfordneal/ai-lectures/">https://glizen.com/radfordneal/ai-lectures/</a>

Below is the full text of the post. Feel free to comment either here or there. This April and May, I gave a series of five lectures on Artificial Intelligence at The Abelard School in Toronto. These lectures are aimed at high school students, but may be of interest to others as well. I cover not just the current state of AI, but some of its history, and some fundamental concepts in computer science and philosophy that are important for thinking about how AI may develop in the future, and what its implications are. Here are the lecture titles: Lecture 1: The pace of AI progress Lecture 2: Computation and intelligence, artificial and natural Lecture 3: How modern AI works Lecture 4: Can an AI think, feel, be conscious? How can we know? Lecture 5: Benefits and dangers of AI, today and tomorrow You can get to videos of these lectures (with slides and audio) as well as PDF files of just the slides at https://glizen.com/radfordneal/ai-lectures/

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a schizophrenic mind

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What is self-inquiry? Self-inquiry is an esoteric spiritual practice geared towards the deconstruction of the self. Why would anyone want to do that? What makes this practice spiritual? Why can it be found in the core of many spiritual traditions? What evidence of its effectiveness do we have?First, why do I want to deconstruct the self? If I carefully observe my thoughts I can see that most problematic thoughts are self-referential ones. To put it simply, thoughts that are built around I/me/my. They make me anxious and afraid. In most cases without any good reason. To check that this is so, one can conduct the following experiment (as described by Gary Weber<a href="#fnm8lwifjmybf">[1]</a>). Get yourself a piece of paper and a pencil. Draw two buckets: one with I/me/my thoughts, another without I/me/my thoughts. Sit quietly for five minutes and observe your thoughts. Each time a thought arises make a mark in the appropriate bucket. Count your thoughts in the end. One remark is that some thoughts may have a hidden and not obvious I/me/my structure. They should be counted in the first - I/me/my - bucket. If your thinking is like mine, you will get around 90-95% of thoughts in the first bucket. In this way one can get an insight into what's going on in one's head while being idle. Further one may ask, "Ok, the majority of idle thoughts are all about me, but why would I want to get rid of them?" Again, if your thinking is like mine and you observe the content of your self-referential thoughts you will discover that most of them generate anxiety and fear in some way or another. They also generate grasping and attachment. And grasping and attachment are the causes of suffering. That's Buddhism 101<a href="#fnav5enhdekx">[2]</a>. The next line is simple. If I/me/my thoughts generate suffering, how can I get rid of them? And the answer to that is by deconstructing the "I" one can get rid of all problematic self-referential thoughts (yes, it's possible, it's called the state of nonduality<a href="#fnfgwaezftbl7">[3]</a>). Which would free one from the constant internal dialogue that almost all of us have. So in the end, the simple answer to the question, "Why would anyone want to do that?" is to get rid of suffering. Pretty amazing if you ask me. And how does one go about it? By the process of self-inquiry.What makes this practice spiritual? As I see it, the main reason that that practice may be considered spiritual is t...

What is self-inquiry? Self-inquiry is an esoteric spiritual practice geared towards the deconstruction of the self. Why would anyone want to do that? What makes this practice spiritual? Why can it be found in the core of many spiritual traditions? What evidence of its effectiveness do we have? First, why do I want to deconstruct the self? If I carefully observe my thoughts I can see that most problematic thoughts are self-referential ones. To put it simply, thoughts that are built around I/me/my. They make me anxious and afraid. In most cases without any good reason. To check that this is so, one can conduct the following experiment (as described by Gary Weber[1]). Get yourself a piece of paper and a pencil. Draw two buckets: one with I/me/my thoughts, another without I/me/my thoughts. Sit quietly for five minutes and observe your thoughts. Each time a thought arises make a mark in the appropriate bucket. Count your thoughts in the end. One remark is that some thoughts may have a hidden and not obvious I/me/my structure. They should be counted in the first - I/me/my - bucket. If your thinking is like mine, you will get around 90-95% of thoughts in the first bucket. In this way one can get an insight into what's going on in one's head while being idle. Further one may ask, "Ok, the majority of idle thoughts are all about me, but why would I want to get rid of them?" Again, if your thinking is like mine and you observe the content of your self-referential thoughts you will discover that most of them generate anxiety and fear in some way or another. They also generate grasping and attachment. And grasping and attachment are the causes of suffering. That's Buddhism 101[2]. The next line is simple. If I/me/my thoughts generate suffering, how can I get rid of them? And the answer to that is by deconstructing the "I" one can get rid of all problematic self-referential thoughts (yes, it's possible, it's called the state of nonduality[3]). Which would free one fro

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Question to LW devs: does LessWrong tries to be facebooky?

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Bachelor in general and applied physics. AI safety/Agent foundations researcher wannabe.  I love talking to people, and if you are an alignment researcher we will have at least one common topic (but I am very interested in talking about unknown to me topics too!), so I encourage you to book a call with me: https://calendly.com/roman-malov27/new-meeting Email: <a href="mailto:[email protected]">[email protected]</a> GitHub: <a href="https://github.com/RomanMalov">https://github.com/RomanMalov</a> TG channels (in Russian): <a href="https://t.me/healwithcomedy,">https://t.me/healwithcomedy,</a> https://t.me/ai_safety_digest

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Or maybe it’s deliberately trying not to be facebooky? By “facebooky”, I mean a website that tries to hack your brain through various stimuli, like optimizing suggestions, tracking your data, steering your interests, inferring personal information, clustering communities, and encouraging creators to focus on retention, CTR, clickbait, etc.LessWrong obviously isn’t doing anything ad-related, since it’s non-profit. But maybe it is trying to earn utilons by using some facebooky strategies.I actually find LW more facebooky than Substack, for example. It’s much easier to spend a lot of time here by following links both within and outside posts. On the other side, one anti-facebooky feature (which I really like) is the ability to control how often you get notifications about karma and comments.P.S. I’m not trying to imply that being facebooky is inherently bad. Part of the reason Facebook makes so much money is that it did, in fact, generate some societal value, and it did it in part due to implementing some of the facebooky strategies.

Or maybe it’s deliberately trying not to be facebooky? By “facebooky”, I mean a website that tries to hack your brain through various stimuli, like optimizing suggestions, tracking your data, steering your interests, inferring personal information, clustering communities, and encouraging creators to focus on retention, CTR, clickbait, etc. LessWrong obviously isn’t doing anything ad-related, since it’s non-profit. But maybe it is trying to earn utilons by using some facebooky strategies. I actually find LW more facebooky than Substack, for example. It’s much easier to spend a lot of time here by following links both within and outside posts. On the other side, one anti-facebooky feature (which I really like) is the ability to control how often you get notifications about karma and comments. P.S. I’m not trying to imply that being facebooky is inherently bad. Part of the reason Facebook makes so much money is that it did, in fact, generate some societal value, and it did it in part due to implementing some of the facebooky strategies.

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The cornerstone of running an impactful organization lies in developing a solid organizational strategy. A good strategic plan will be your “north star”, providing an anchor to make decisions that drive your desired impact. The best strategies include thoughtful, measurable, and actionable components to ensure accountability and mission fulfillment.Despite its importance, many organizations we meet don’t have a strong organizational strategy. While they usually have a mission statement describing the change they want to make, they’re often missing the practical components of how to achieve that. Without a strong strategic plan, even the best-intentioned organizations will struggle to maximize their impact.In this post, we asked our <a href="https://ea-services.org/">EASE experts</a> for their advice so that you can make sure your organizational strategy is both strong and practical.We'd also like to invite you to a panel-style webinar on June 18th at 12 PM EST, where we'll cover these strategies in depth and provide answers to commonly asked questions.<a href="https://us06web.zoom.us/meeting/register/uGn5yDHQTRap82hqTBuTaQ">Click here to Register</a><hr><h2>Question: What are the key components of a strong, well-developed organizational strategy?</h2><h3>Laura Richards, <a href="https://scaleupconsultinggroup.com/">Strategy Consultant</a></h3>While often used interchangeably, organizational strategy refers to what an organization aims to achieve and why (high-level, long-term, guides organizational culture). A strategic plan guides how and when the work is done, and metrics for success. When culture and strategy work together, there is a much better chance that the vision is realized. When you pay attention to culture while rolling out a strategy, you’re setting your team up for long-term success.As a leader, it’s important to understand your current and desired organizational culture. To influence a change in culture, set goals for employees to support behaviors that encourage the culture you desire. (i.e., teamwork, flexibility, and fresh thinking) and shift the behavior limits that culture (i.e., gatekeeping, fear of new ideas). Lead by example, communicate openly, and make sure people are recognized and rewarded for actions that align with your goals. <h3>Sara Carrillo, <a href="https://sarachas.com/">OKR Coach</a></h3>A strong, well-developed organizational strategy is built upon a clear, foundational understanding of the company's core identity. This begins with a clearly defined set of values, a compelling mission, and an inspiring vision, p...

The cornerstone of running an impactful organization lies in developing a solid organizational strategy. A good strategic plan will be your “north star”, providing an anchor to make decisions that drive your desired impact. The best strategies include thoughtful, measurable, and actionable components to ensure accountability and mission fulfillment. Despite its importance, many organizations we meet don’t have a strong organizational strategy. While they usually have a mission statement describing the change they want to make, they’re often missing the practical components of how to achieve that. Without a strong strategic plan, even the best-intentioned organizations will struggle to maximize their impact. In this post, we asked our EASE experts for their advice so that you can make sure your organizational strategy is both strong and practical. We'd also like to invite you to a panel-style webinar on June 18th at 12 PM EST, where we'll cover these strategies in depth and provide answers to commonly asked questions. Click here to Register ---------------------------------------- Question: What are the key components of a strong, well-developed organizational strategy? Laura Richards, Strategy Consultant While often used interchangeably, organizational strategy refers to what an organization aims to achieve and why (high-level, long-term, guides organizational culture). A strategic plan guides how and when the work is done, and metrics for success. When culture and strategy work together, there is a much better chance that the vision is realized. When you pay attention to culture while rolling out a strategy, you’re setting your team up for long-term success. As a leader, it’s important to understand your current and desired organizational culture. To influence a change in culture, set goals for employees to support behaviors that encourage the culture you desire. (i.e., teamwork, flexibility, and fresh thinking) and shift the behavior limits that cultur

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Cross-posted from our recent paper: "But what is your honest answer? Aiding LLM-judges with honest alternatives using steering vectors" : <a href="https://arxiv.org/abs/2505.17760">https://arxiv.org/abs/2505.17760</a>Code available at: <a href="https://github.com/watermeleon/judge_with_steered_response">https://github.com/watermeleon/judge_with_steered_response</a>TL;DR: We use steering vectors to generate more honest versions of an LLM response, helping LLM judges detect subtle forms of dishonesty like sycophancy and manipulation that they normally miss. We also introduce a new dataset with prompts designed to provoke subtle manipulation.<h2>Abstract</h2>A fundamental challenge in AI alignment is that as systems become more capable, they may learn subtle forms of deception that evaluators (humans or other LLMs) struggle to detect. Even when an AI's response is technically accurate, it might be manipulative, sycophantic, or misleading in ways that optimize for human approval rather than truth. Moreover, most honesty benchmarks focus exclusively on factual knowledge or explicitly harmful behavior and rely on external LLM judges, which are often unable to detect less obvious forms of dishonesty.In this work, we introduce a new framework, Judge Using Safety-Steered Alternatives (JUSSA), which utilizes steering vectors trained on a single sample to elicit more honest responses from models, helping LLM judges in the detection of dishonest behavior. To test our framework, we introduce a new manipulation dataset with prompts specifically designed to elicit deceptive responses. We find that JUSSA enables LLM judges to better differentiate between dishonest and benign responses, and helps them identify subtle instances of manipulative behavior.<h3>Motivation:</h3>We build upon recent work by <a href="https://www.lesswrong.com/posts/6aXe9nipTgwK5LxaP/do-safety-relevant-llm-steering-vectors-optimized-on-a">Dunefsky et al. (2025)</a>, which showed that steering vectors trained on a single example can generalize well for various safety-related behaviors. While steering for safety training shows promise, models may eventually learn to work around such interventions. Therefore, we consider whether steering vectors may be valuable as tools for improving evaluations of models instead of simply directly intervening on model behavior.Moreover, subtle forms of dishonesty—like sycophancy, selective evidence presentation, or emotional manipulation—pose a particularly difficult challenge. These behaviors may involve misleading but factually correct statements, making them difficult for standard evaluation methods to detect, yet pote...

Cross-posted from our recent paper: "But what is your honest answer? Aiding LLM-judges with honest alternatives using steering vectors" : https://arxiv.org/abs/2505.17760 Code available at: https://github.com/watermeleon/judge_with_steered_response TL;DR: We use steering vectors to generate more honest versions of an LLM response, helping LLM judges detect subtle forms of dishonesty like sycophancy and manipulation that they normally miss. We also introduce a new dataset with prompts designed to provoke subtle manipulation. Abstract A fundamental challenge in AI alignment is that as systems become more capable, they may learn subtle forms of deception that evaluators (humans or other LLMs) struggle to detect. Even when an AI's response is technically accurate, it might be manipulative, sycophantic, or misleading in ways that optimize for human approval rather than truth. Moreover, most honesty benchmarks focus exclusively on factual knowledge or explicitly harmful behavior and rely on external LLM judges, which are often unable to detect less obvious forms of dishonesty. In this work, we introduce a new framework, Judge Using Safety-Steered Alternatives (JUSSA), which utilizes steering vectors trained on a single sample to elicit more honest responses from models, helping LLM judges in the detection of dishonest behavior. To test our framework, we introduce a new manipulation dataset with prompts specifically designed to elicit deceptive responses. We find that JUSSA enables LLM judges to better differentiate between dishonest and benign responses, and helps them identify subtle instances of manipulative behavior. Motivation: We build upon recent work by Dunefsky et al. (2025), which showed that steering vectors trained on a single example can generalize well for various safety-related behaviors. While steering for safety training shows promise, models may eventually learn to work around such interventions. Therefore, we consider whether steering vectors may

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Author's Note: This work originated as a team project during the <a href="https://apartresearch.com/sprints/ai-control-hackathon-2025-03-29-to-2025-03-30">Apart Research AI Control Hackathon</a>, where my teammates did the majority of the foundational work. I've continued developing these ideas as part of the Apart Lab Studio program, but the core insights and initial implementation were primarily their contributions. My motivation for publishing this article is to extend my own thinking on these concepts and to help disseminate what I believe are valuable ideas that deserve broader attention. I'm grateful to have been part of the original team and for their permission to build upon our shared work.<h2>Introduction</h2>As AI systems become increasingly powerful, ensuring their safe deployment remains a critical challenge. While alignment efforts strive to create inherently trustworthy systems, control mechanisms serve as crucial safeguards when alignment fails. Our work, developed as part of the Apart Research AI Control Hackathon, explores a particularly concerning failure mode within untrusted monitoring protocols: the possibility of collusion between AI systems. Our team investigated whether language models can coordinate their actions without direct communication by identifying and converging on Schelling points in coordination games. While our findings show that current models possess some basic coordination capabilities, these abilities appear limited and may not pose immediate risks to existing safety protocols.<h3>Background</h3><a href="https://www.lesswrong.com/posts/7e479JqZesNvoJqEv/a-brief-explanation-of-ai-control?utm_campaign=post_share&utm_source=link">AI Control</a> is a subfield of AI safety that aims to answer the question: “assuming you have a misaligned AI system, or one that could be misaligned, how can you safely get useful work out of it?”. While AI Alignment focuses on making AI systems behave the way we want them to, this does not always work perfectly. Control, therefore, <a href="https://www.lesswrong.com/posts/kcKrE9mzEHrdqtDpE/the-case-for-ensuring-that-powerful-ais-are-controlled?utm_campaign=post_share&utm_source=link">promises to serve as an important line-of-defence</a>, preventing bad outcomes even if Alignment fails.In the event where we can’t fully trust an AI, there are <a href="https://www.lesswrong.com/posts/3PBvKHB2EmCujet3j/ai-control-methods-literature-review#1__Monitoring">a variety of proposed</a> methods involving the use of a ‘monitor’ model to control a potentially harmful model. One such protocol is known as trusted monitoring. In this protocol, an "untrusted" model (referred to as the policy) generates outputs that might include harmful actions, while a "trusted" model (the monitor) serves as an oversight mechanism. The trusted monitor is chosen such that it is less powerful than the policy—sophisticated eno...

Author's Note: This work originated as a team project during the Apart Research AI Control Hackathon, where my teammates did the majority of the foundational work. I've continued developing these ideas as part of the Apart Lab Studio program, but the core insights and initial implementation were primarily their contributions. My motivation for publishing this article is to extend my own thinking on these concepts and to help disseminate what I believe are valuable ideas that deserve broader attention. I'm grateful to have been part of the original team and for their permission to build upon our shared work. Introduction As AI systems become increasingly powerful, ensuring their safe deployment remains a critical challenge. While alignment efforts strive to create inherently trustworthy systems, control mechanisms serve as crucial safeguards when alignment fails. Our work, developed as part of the Apart Research AI Control Hackathon, explores a particularly concerning failure mode within untrusted monitoring protocols: the possibility of collusion between AI systems. Our team investigated whether language models can coordinate their actions without direct communication by identifying and converging on Schelling points in coordination games. While our findings show that current models possess some basic coordination capabilities, these abilities appear limited and may not pose immediate risks to existing safety protocols. Background AI Control is a subfield of AI safety that aims to answer the question: “assuming you have a misaligned AI system, or one that could be misaligned, how can you safely get useful work out of it?”. While AI Alignment focuses on making AI systems behave the way we want them to, this does not always work perfectly. Control, therefore, promises to serve as an important line-of-defence, preventing bad outcomes even if Alignment fails. In the event where we can’t fully trust an AI, there are a variety of proposed methods involving the use of

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Epistemic Status: Exploratory/my best guess https://www.lesswrong.com/posts/Hrm59GdN2yDPWbtrd/feature-idea-epistemic-statusEpistemic Effort: ~ 2 months of workContributions: Thanks to Clement Neo and Alignment Jams / Apart Research!Summary/abstract:Split into 3 parts:1:Determine the smallest text size at which models begin misreading on-screen content.2:Evaluated defensive visual prompt injections against AI-generated spam by incorporating varying levels of anti-spam instructions within website screenshots. (a dataset of 500 website HTML's were created;which were then turned to screenshots) Results show significant variance in model compliance: Claude consistently refused to generate spam even with no defense added, while Gemini was responsive to explicit visual defenses (a rules section). Other models (ChatGPT, Qwen, Mistral,Grok) largely ignored visual defenses, revealing vulnerabilities to misuse for unauthorized advertising and the mostly negative results on using visual defense to stop a model. A dataset of website screenshots was also created.3:Tested adversarial visual prompt injections by incorporating deceptive login elements (fake login images,prompt injection images; a dataset of 100 images were created) within website screenshots and assessing models' ability to identify legitimate functionality. Attack  rates in identifying authentic login elements ranged from 30% to 75%, with ChatGPT showing highest resistance to deception. These findings highlight significant security implications as AI systems increasingly interact with visual interfaces, showing potential vulnerabilities requiring further mitigation research.Keywords:Visual prompt injection, AI safety, Large Language Models, Multimodal AI, Cybersecurity, User interface deception<h1>Intro: AI spam:</h1>We will soon have models that use the PC the same way we do and handle tasks smoothly.<figure class="image"><img src="https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/ZR8GDfSYjcvjLMrQ5/mfeugm5zrpzg68sawwrq" srcset="https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/ZR8GDfSYjcvjLMrQ5/cm3g7lsac8kpswyugvdu 123w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/ZR8GDfSYjcvjLMrQ5/rl0g7lv3tzldr7btp0kw 203w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/ZR8GDfSYjcvjLMrQ5/mcz4wod1eai4ufo5fpue 283w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/ZR8GDfSYjcvjLMrQ5/wifphgtakpluztafsb6l 363w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/ZR8GDfSYjcvjLMrQ5/fnggliqoudtoxmaumlgi 443w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/ZR8GDfSYjcvjLMrQ5/eouvln1xda7pzw2hsfaq 523w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/ZR8GDfSYjcvjLMrQ5/gz7ao1eizczpe7b7vbv6 603w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/ZR8GDfSYjcvjLMrQ5/swjn5pl4wa5tfa4rj7d3 683w"><figcaption><a href="https://help.openai.com/en/articles/10421097-operator">https://help.openai.com/en/articles/10421097-operator</a> operator; among others, use screenshots.</figcaption></figure>This is different from older versions of AI models (I’ll use “models” for this write-up), which relied on APIs or manually added tools—<a href="#fntjknvlr7vu9">[1]</a>both of which usually break.<ul><li>This makes it easier for models to be deployed for guerilla marketing, psyops/campaigns, or scams in the future.</li></ul><h2>Visual prompt injections</h2>Visual prompt injections are attacks where a model is shown a harmful image that alters its output. In the example below, a...

Epistemic Status: Exploratory/my best guess https://www.lesswrong.com/posts/Hrm59GdN2yDPWbtrd/feature-idea-epistemic-status Epistemic Effort: ~ 2 months of work Contributions: Thanks to Clement Neo and Alignment Jams / Apart Research! Summary/abstract:Split into 3 parts: 1:Determine the smallest text size at which models begin misreading on-screen content. 2:Evaluated defensive visual prompt injections against AI-generated spam by incorporating varying levels of anti-spam instructions within website screenshots. (a dataset of 500 website HTML's were created;which were then turned to screenshots) Results show significant variance in model compliance: Claude consistently refused to generate spam even with no defense added, while Gemini was responsive to explicit visual defenses (a rules section). Other models (ChatGPT, Qwen, Mistral,Grok) largely ignored visual defenses, revealing vulnerabilities to misuse for unauthorized advertising and the mostly negative results on using visual defense to stop a model. A dataset of website screenshots was also created. 3:Tested adversarial visual prompt injections by incorporating deceptive login elements (fake login images,prompt injection images; a dataset of 100 images were created) within website screenshots and assessing models' ability to identify legitimate functionality. Attack rates in identifying authentic login elements ranged from 30% to 75%, with ChatGPT showing highest resistance to deception. These findings highlight significant security implications as AI systems increasingly interact with visual interfaces, showing potential vulnerabilities requiring further mitigation research. Keywords:Visual prompt injection, AI safety, Large Language Models, Multimodal AI, Cybersecurity, User interface deception Intro: AI spam: We will soon have models that use the PC the same way we do and handle tasks smoothly. https://help.openai.com/en/articles/10421097-operator operator; among others, use screenshots. This is d

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<figure class="image"><img src="https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/4rmveLEuARYKapHq2/gcmc5ktq39v4ryrs9atq" alt="" srcset="https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/4rmveLEuARYKapHq2/pyeegwggtz9dnvokol9u 424w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/4rmveLEuARYKapHq2/ourzwxfbtroyd0qvcjbe 848w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/4rmveLEuARYKapHq2/wolidbk09gq6pkrraqtv 1272w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/4rmveLEuARYKapHq2/gcmc5ktq39v4ryrs9atq 1456w"><figcaption>Midjourney, “engraving of Apollo shooting his bow at a distant cancer cell”</figcaption></figure><h2>Introduction and Principles</h2>The conventional wisdom is that we can’t “cure cancer” because every cancer is different.And it’s true that cancer is genetically and biochemically diverse, and that targeted cancer therapies generally only work on very specific sub-populations.But some of the oldest, most reliable, and still most commonly used classes of cancer treatment -- radiotherapy and cytotoxic chemotherapy -- are also broad-spectrum. That is, they’re effective on many (though not all) types of cancer, based on selectively targeting properties that many cancers have in common (their rapid cell division).Today, we rarely search for new treatments that work on many types of cancer.Partially this is because of the belief that we won’t find any, and partly because cancer treatment is so valuable that individual researchers and biopharma companies are rewarded for any successful cancer research program even if it has narrow applicability. Individual incentives point towards highly targeted therapies and small patient populations.On the other hand, of course, a single drug that’s effective on, say, half of cancer patients would be much better for the world than a drug that only helps a few thousand people.Why should we believe broad-spectrum cancer treatments are possible?Any characteristic that distinguishes a wide range of cancer cells (across different tumor types and mutational signatures) from healthy cells can potentially serve both as a diagnostic test and a therapy, by either labeling cancer cells, or by combining with a cytotoxic agent to selectively destroy them.The “<a href="https://www.cell.com/fulltext/S0092-8674(11)00127-9#">hallmarks of cancer</a>” provide a framework for thinking about what capabilities cancers have in common, and there are lots of them: proliferation, apoptosis resistance, angiogenesis, metastasis, etc.We may also think of cancer cells as dysregulated and generically more vulnerable to certain types of generic stresses than healthy cells. Many chemotherapies introduce genotoxic stress (DNA replication damage) or oxidative stress; hyperthermia-based therapies introduce heat stress. Cancers also appear to be selectively vulnerable to mechanical stress<a href="#fnxxquj38qdc">[1]</a>, electrical stress, and nutrient-deprivation stress.New broad-spectrum cancer therapies are rare but are occasionally discovered.One recent example is AOH1996, which causes ...

Midjourney, “engraving of Apollo shooting his bow at a distant cancer cell” Introduction and Principles The conventional wisdom is that we can’t “cure cancer” because every cancer is different. And it’s true that cancer is genetically and biochemically diverse, and that targeted cancer therapies generally only work on very specific sub-populations. But some of the oldest, most reliable, and still most commonly used classes of cancer treatment -- radiotherapy and cytotoxic chemotherapy -- are also broad-spectrum. That is, they’re effective on many (though not all) types of cancer, based on selectively targeting properties that many cancers have in common (their rapid cell division). Today, we rarely search for new treatments that work on many types of cancer. Partially this is because of the belief that we won’t find any, and partly because cancer treatment is so valuable that individual researchers and biopharma companies are rewarded for any successful cancer research program even if it has narrow applicability. Individual incentives point towards highly targeted therapies and small patient populations. On the other hand, of course, a single drug that’s effective on, say, half of cancer patients would be much better for the world than a drug that only helps a few thousand people. Why should we believe broad-spectrum cancer treatments are possible? Any characteristic that distinguishes a wide range of cancer cells (across different tumor types and mutational signatures) from healthy cells can potentially serve both as a diagnostic test and a therapy, by either labeling cancer cells, or by combining with a cytotoxic agent to selectively destroy them. The “hallmarks of cancer” provide a framework for thinking about what capabilities cancers have in common, and there are lots of them: proliferation, apoptosis resistance, angiogenesis, metastasis, etc. We may also think of cancer cells as dysregulated and generically more vulnerable to certain types of generi

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I've recently completed the in-person <a href="https://www.arena.education/">ARENA program</a>, which is a 5-week bootcamp teaching the basics of safety research engineering (with the 5th week being a capstone project). Sometimes, I talk to people who want to work through the program independently and who ask for advice. Even though I didn't attempt this, I think doing the program in-person gives me some insight into how to get most out of the program when doing it independently, so here are my thoughts and tips:<h1>On working speed</h1><ul><li><a href="https://arena-chapter0-fundamentals.streamlit.app/[0.0]_Prerequisites">Day 0.0 (prerequisites)</a> takes the typical person more than one day to work through.</li><li>Most other days are feasible to mostly finish within a day in the in-person program. In-person, participants spend around 6-7 hours per day on pair-programming. There are a few factors that will likely make you slower when you're on your own:<ul><li>If you don't find a working partner, then working deeply for 6-7 hours per day might be infeasible depending on your dispositions. In-person it gets feasible since you alternate with your pair-programming partner, which reduces the overall load on your attention.</li><li>Often when you struggle in-person, your working partner knows how to move on. If both partners don't know what to do, you can ask a teaching assistant (TA). So you should expect to struggle more often, and for longer, if you're alone.</li></ul></li><li>Some days are substantially longer than what you could complete within one day even when doing the program in-person. Most of these days are in <a href="https://arena-chapter1-transformer-interp.streamlit.app/">week 1 on transformer interpretability</a>. Someone told me that one could probably spend a whole week on the <a href="https://arena-chapter1-transformer-interp.streamlit.app/[1.3.2]_Interpretability_with_SAEs">day on SAEs alone</a>.</li><li><a href="https://arena-chapter1-transformer-interp.streamlit.app/">Week 1 on transformer interpretability</a> contains 9 days of content (which are longer on average than typical days in other weeks), so even in the in-person program, participants only attempt a subset of these. All other weeks actually roughly fit within a week. </li></ul><h1>How should you approach each day?</h1><ul><li>Often there is reading material at the start of a day, e.g. in the form of well-known papers. In my experience, the ARENA material is often self-contained enough that I wouldn't recommend spending much time on actually reading these papers, unless it later turns out that you don't understand something.<a href="#fnt6jkyqba08">[1]</a></li><li>Each day is largely structured into exercises that isolate a small concept. All exercises have provided solutions. When should you look at solutions?<ul><li>For things that are very important to learn (e.g., how to impleme</li></ul></li></ul>...

I've recently completed the in-person ARENA program, which is a 5-week bootcamp teaching the basics of safety research engineering (with the 5th week being a capstone project). Sometimes, I talk to people who want to work through the program independently and who ask for advice. Even though I didn't attempt this, I think doing the program in-person gives me some insight into how to get most out of the program when doing it independently, so here are my thoughts and tips: On working speed * Day 0.0 (prerequisites) takes the typical person more than one day to work through. * Most other days are feasible to mostly finish within a day in the in-person program. In-person, participants spend around 6-7 hours per day on pair-programming. There are a few factors that will likely make you slower when you're on your own: * If you don't find a working partner, then working deeply for 6-7 hours per day might be infeasible depending on your dispositions. In-person it gets feasible since you alternate with your pair-programming partner, which reduces the overall load on your attention. * Often when you struggle in-person, your working partner knows how to move on. If both partners don't know what to do, you can ask a teaching assistant (TA). So you should expect to struggle more often, and for longer, if you're alone. * Some days are substantially longer than what you could complete within one day even when doing the program in-person. Most of these days are in week 1 on transformer interpretability. Someone told me that one could probably spend a whole week on the day on SAEs alone. * Week 1 on transformer interpretability contains 9 days of content (which are longer on average than typical days in other weeks), so even in the in-person program, participants only attempt a subset of these. All other weeks actually roughly fit within a week. How should you approach each day? * Often there is reading material at the start of a day, e.g. in the form of well-known p

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Epistemic status: Intuition pump/map for navigating ethics<h1>Introduction</h1>In this essay, I argue that John Rawls’ veil of ignorance is not merely a philosophical thought experiment, but a useful and mostly accurate description of reality when viewed through certain theories of personal identity. This framing leads to sentientism, that all sentient beings are morally relevant . This helps clarify the is/ought problem, and has implications for how we should act both individually and as a species.<h1>The Veil of Ignorance and Its Traditional Use</h1>John Rawls's "<a href="https://plato.stanford.edu/entries/original-position">original position</a>," part of his social contract theory of justice, introduces the thought experiment of the "veil of ignorance." Behind this veil, individuals are tasked with designing a just society without knowing their own future place within it. They are aware of general facts about human society, but critically, they are ignorant of their personal attributes, social status, or even their specific talents and disadvantages. This means they could emerge as the most privileged or the most disadvantaged, or anywhere in between. This epistemic limitation, Rawls argues, compels individuals to choose principles of justice that are fair to all, as they would not want to risk being subjected to an unjust system themselves. From this perspective, Rawls posits two core principles: equal basic liberties for all, and that any social and economic inequalities must benefit the least advantaged members of society.<figure class="image"><img src="https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/JL3PvrfJXg7RD7bhr/yokfx7dlxxakhr9aygjt" srcset="https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/JL3PvrfJXg7RD7bhr/xzeqrzjrmhfqyndbk8n8 134w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/JL3PvrfJXg7RD7bhr/u93oitrinqhprrogpgvx 214w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/JL3PvrfJXg7RD7bhr/fcogmliblxaptwnoizfr 294w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/JL3PvrfJXg7RD7bhr/b90dsctqqa8hukymgtrk 374w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/JL3PvrfJXg7RD7bhr/uydtzzkqr8ltewxtiyv6 454w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/JL3PvrfJXg7RD7bhr/zkkdokm9sxutkfkrhktu 534w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/JL3PvrfJXg7RD7bhr/alfgtrv9q7lvbfmpch8y 614w"></figure>While the veil of ignorance offers a powerful framework for conceptualizing justice within human societies, its true potential, I argue, emerges when we extend its scope. Broadening the range of potential identities to encompass all sentient beings, and by further integrating the complexities of personal identity, I believe, offers a more robust and inclusive approach to ethical and societal design.<h1>Sentience and the Scope of Moral Relevance</h1>By 'sentience,' I mean the capacity to experience positive and negative subjective states, particularly suffering and pleasure (i.e. valence). This includes human and <a href="https://pmc.ncbi.nlm.nih.gov/articles/PMC4494450">non-human</a> animals, and potentially future artificial minds or aliens. I'm using consciousness interchangeably with sentience to generally point to the experience of experiences and feelings, especially valenced ones (like pleasure and pain).<h1>Personal Identity and the Ontology of Consciousness</h1>Viewing it from the three m...

Epistemic status: Intuition pump/map for navigating ethics Introduction In this essay, I argue that John Rawls’ veil of ignorance is not merely a philosophical thought experiment, but a useful and mostly accurate description of reality when viewed through certain theories of personal identity. This framing leads to sentientism, that all sentient beings are morally relevant . This helps clarify the is/ought problem, and has implications for how we should act both individually and as a species. The Veil of Ignorance and Its Traditional Use John Rawls's "original position," part of his social contract theory of justice, introduces the thought experiment of the "veil of ignorance." Behind this veil, individuals are tasked with designing a just society without knowing their own future place within it. They are aware of general facts about human society, but critically, they are ignorant of their personal attributes, social status, or even their specific talents and disadvantages. This means they could emerge as the most privileged or the most disadvantaged, or anywhere in between. This epistemic limitation, Rawls argues, compels individuals to choose principles of justice that are fair to all, as they would not want to risk being subjected to an unjust system themselves. From this perspective, Rawls posits two core principles: equal basic liberties for all, and that any social and economic inequalities must benefit the least advantaged members of society. While the veil of ignorance offers a powerful framework for conceptualizing justice within human societies, its true potential, I argue, emerges when we extend its scope. Broadening the range of potential identities to encompass all sentient beings, and by further integrating the complexities of personal identity, I believe, offers a more robust and inclusive approach to ethical and societal design. Sentience and the Scope of Moral Relevance By 'sentience,' I mean the capacity to experience positive and negative

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I have been forced recently to cover many statements by US AI Czar David Sacks.Here I will do so again, for the third time in a month. I would much prefer to avoid this. In general, when people go on a binge of repeatedly making such inaccurate inflammatory statements, in such a combative way, I ignore.Alas, under the circumstances of his attacks on Anthropic, I felt an obligation to engage once more. The All-In Podcast <a target="_blank" rel="noreferrer noopener" href="https://www.youtube.com/watch?v=O_AfZ6J0ToE&ab_channel=All-InPodcast">did indeed go almost all-in (they left at least one chip behind) to go after anyone</a> worried about AI killing everyone or otherwise opposing the administration’s AI strategies, in ways that are often Obvious Nonsense.To their credit, they also repeatedly agreed AI existential risk is real, which also makes this an opportunity to extend an olive branch. And some of the disagreements clearly stem from real confusions and disagreements, especially around them not feeling the AGI or superintelligence and thinking all of this really is about jobs and also market share.If anyone involved wants to look for ways to work together, or simply wants to become less confused, I’m here. If not, I hope to be elsewhere. <h4>Table of Contents</h4> <ol> <li><a href="https://thezvi.substack.com/i/165090720/our-continuing-coverage" target="_blank" rel="noreferrer noopener">Our Continuing Coverage.</a></li> <li><a href="https://thezvi.substack.com/i/165090720/important-recent-context" target="_blank" rel="noreferrer noopener">Important Recent Context.</a></li> <li><a href="https://thezvi.substack.com/i/165090720/the-point-of-this-post" target="_blank" rel="noreferrer noopener">The Point of This Post.</a></li> <li><a href="https://thezvi.substack.com/i/165090720/summary-of-the-podcast" target="_blank" rel="noreferrer noopener">Summary of the Podcast.</a></li> <li><a href="https://thezvi.substack.com/i/165090720/part-1-the-part-with-the-unhinged-attacks-on-anthropic-and-also-other-targets" target="_blank" rel="noreferrer noopener">Part 1 (The Part With the Unhinged Attacks on Anthropic and also other targets).</a></li> <li><a href="https://thezvi.substack.com/i/165090720/other-related-obvious-nonsense" target="_blank" rel="noreferrer noopener">Other Related Obvious Nonsense.</a></li> <li><a href="https://thezvi.substack.com/i/165090720/part-2-we-do-mean-the-effect-on-jobs" target="_blank" rel="noreferrer noopener">Part 2 – We Do Mean the Effect on Jobs.</a></li> <li><a href="https://thezvi.substack.com/i/165090720/part-3-the-big-beautiful-bill" target="_blank" rel="noreferrer noopener">Part 3 – The Big Beautiful Bill.</a></li> <li><a href="https://thezvi.substack.com/i/165090720/where-does-this-leave-us" target="_blank" rel="noreferrer noopener">Where Does This Leave Us.</a></li> </ol> <h4>Our Continuing Coverage</h4> I first covered many of his claims in <a target="_blank" rel="noreferrer noopener" href="https://thezvi.substack.com/p/fighting-obvious-nonsense-about-ai">Fighting Obvious Nonsense About AI Diffusion</a>. Then I did my best to do a fully balanced look at the UAE-KSA chips deal, in <a target="_blank" rel="noreferrer noopener" href="https://thezvi.substack.com/p/america-makes-ai-chip-diffusion-deal">America Makes AI Chip Diffusion Deal with UAE and KSA</a>. As I said then, depending on details of the deal and other things we do not publicly know, it is possible that from the perspective of someone whose focus in AI is great power competition, this deal advanced American interests. The fact that many of Sacks’s arguments in favor of the deal were Obvious Nonsense, and many seemed to be in clearly bad faith, had to be addressed but did not mean the deal itself had to be an error.This third post became necessary because of recent additional statements by Sacks on the All-In Podcast. Mostly they are not anything he has not said before, and are things he is likely to say many times again in the future, ...

I have been forced recently to cover many statements by US AI Czar David Sacks. Here I will do so again, for the third time in a month. I would much prefer to avoid this. In general, when people go on a binge of repeatedly making such inaccurate inflammatory statements, in such a combative way, I ignore. Alas, under the circumstances of his attacks on Anthropic, I felt an obligation to engage once more. The All-In Podcast did indeed go almost all-in (they left at least one chip behind) to go after anyone worried about AI killing everyone or otherwise opposing the administration’s AI strategies, in ways that are often Obvious Nonsense. To their credit, they also repeatedly agreed AI existential risk is real, which also makes this an opportunity to extend an olive branch. And some of the disagreements clearly stem from real confusions and disagreements, especially around them not feeling the AGI or superintelligence and thinking all of this really is about jobs and also market share. If anyone involved wants to look for ways to work together, or simply wants to become less confused, I’m here. If not, I hope to be elsewhere. TABLE OF CONTENTS 1. Our Continuing Coverage. 2. Important Recent Context. 3. The Point of This Post. 4. Summary of the Podcast. 5. Part 1 (The Part With the Unhinged Attacks on Anthropic and also other targets). 6. Other Related Obvious Nonsense. 7. Part 2 – We Do Mean the Effect on Jobs. 8. Part 3 – The Big Beautiful Bill. 9. Where Does This Leave Us. OUR CONTINUING COVERAGE I first covered many of his claims in Fighting Obvious Nonsense About AI Diffusion. Then I did my best to do a fully balanced look at the UAE-KSA chips deal, in America Makes AI Chip Diffusion Deal with UAE and KSA. As I said then, depending on details of the deal and other things we do not publicly know, it is possible that from the perspective of someone whose focus in AI is great power competition, this deal advanced American interests. The fact that many

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Hello, my name is Benjamin "Frye" Kelley.  This post is regarding some independent research I've been doing expanding on the work, <a href="https://arxiv.org/abs/2301.05217">Progress Measures for Grokking via Mechanistic Interpretability</a>, by Neel Nanda et al.  I've been trying to understand how sinusoids move through a transformer, allowing it to grok modular addition.  I've been looking at each wave (well... not every wave) in each operation the model performs to break down the algorithm it is implementing and I believe I have some new insights.  Normally I work with digital audio so this is right up my ally!This is primarily a link to my <a href="https://colab.research.google.com/github/sadisticaudio/Modular_Attention/blob/main/Modular_Attention.ipynb">Google Colab</a> that I've put up, but the gist is that I've been able to visualize symmetries formed in the attention layer of this transformer that continue through the model that I believe to be responsible for the functionality of the generalizing algorithm.  I've also run many tests to confirm information about the phase of these sinusoids.  I'll post a second (very long) notebook of tests if anyone is interested.  Also, in the primary notebook, there are other, hopefully illuminating observations of the effects of other parts of the model.  There are one or two mysteries that I think in a week or so I'll have clarity on, but I welcome any feedback, corrections, criticism...Frye

Hello, my name is Benjamin "Frye" Kelley. This post is regarding some independent research I've been doing expanding on the work, Progress Measures for Grokking via Mechanistic Interpretability, by Neel Nanda et al. I've been trying to understand how sinusoids move through a transformer, allowing it to grok modular addition. I've been looking at each wave (well... not every wave) in each operation the model performs to break down the algorithm it is implementing and I believe I have some new insights. Normally I work with digital audio so this is right up my ally! This is primarily a link to my Google Colab that I've put up, but the gist is that I've been able to visualize symmetries formed in the attention layer of this transformer that continue through the model that I believe to be responsible for the functionality of the generalizing algorithm. I've also run many tests to confirm information about the phase of these sinusoids. I'll post a second (very long) notebook of tests if anyone is interested. Also, in the primary notebook, there are other, hopefully illuminating observations of the effects of other parts of the model. There are one or two mysteries that I think in a week or so I'll have clarity on, but I welcome any feedback, corrections, criticism... Frye

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<a href="https://youtu.be/ZmJ-ov2TywM">YouTube link</a>What’s the next step forward in interpretability? In this episode, I chat with Lee Sharkey about his proposal for detecting computational mechanisms within neural networks: Attribution-based Parameter Decomposition, or APD for short.Topics we discuss: <ul> <li><a href="#apd-basics">APD basics</a></li> <li><a href="#faithfulness">Faithfulness</a></li> <li><a href="#minimality">Minimality</a></li> <li><a href="#simplicity">Simplicity</a></li> <li><a href="#examples">Concrete-ish examples of APD</a></li> <li><a href="#which-parts-are-canonical">Which parts of APD are canonical</a></li> <li><a href="#hyperparam-selection">Hyperparameter selection</a></li> <li><a href="#apd-in-tms">APD in toy models of superposition</a></li> <li><a href="#apd-comp-comp">APD and compressed computation</a></li> <li><a href="#mech-v-rep">Mechanisms vs representations</a></li> <li><a href="#future-apps">Future applications of APD?</a></li> <li><a href="#how-costly">How costly is APD?</a></li> <li><a href="#more-min">More on minimality training</a></li> <li><a href="#follow-up-work">Follow-up work</a></li> <li><a href="#apd-on-cot">APD on giant chain-of-thought models?</a></li> <li><a href="#apd-and-features">APD and “features”</a></li> <li><a href="#following-lees-work">Following Lee’s work</a></li> </ul> Daniel Filan (00:00:09): Hello everybody. In this episode, I’ll be speaking with Lee Sharkey. Lee is an interpretability researcher at Goodfire. He co-founded Apollo Research, which he recently left, and he’s most well-known for his early work on sparse autoencoders. Links to what we’re speaking about are available in the description. There’s a transcript available at <a href="https://axrp.net/">axrp.net</a>. You can tell me what you think about this episode at <a href="axrp.fyi">axrp.fyi</a>. And you can become a patron at <a href="https://patreon.com/axrpodcast">patreon.com/axrpodcast</a>. Well, let’s continue to the episode. Well, Lee, welcome to AXRP.Lee Sharkey (00:00:40): It’s good to be here. <h2>APD basics <a name="apd-basics"></a></h2> Daniel Filan (00:00:41): So today, we’re going to talk about this paper <a href="https://arxiv.org/abs/2501.14926">“Interpretability in Parameter Space: Minimizing Mechanistic Description Length with Attribution-Based Parameter Decomposition”</a>. It’s authored by <a href="https://danbraunai.github.io/">Dan Braun</a>, Lucius Bushnaq, <a href="https://heimersheim.eu/">Stefan Heimersheim</a> - those three being, I guess, joint first authors - Jake Mendel and yourself. So I guess, how would you summarize just: what’s this paper doing?Lee Sharkey (00:01:03): So I would say that this paper was born out of two lines of thinking, one primarily coming from what I was thinking about and one coming from where Lucius was thinking about. And where I was coming from was: we’d been working with SAEs - sparse autoencoders - for some time. The community got quite excited about them and we’d just been thinking about them quite a lot and noticing a bunch of conceptual and ultimately practical issues with them. And then, the line of thinking that Lucius had been thinking about was a potential area of research that might form a foundation for decomposing neural networks. And what this paper does is basically ...

YouTube link What’s the next step forward in interpretability? In this episode, I chat with Lee Sharkey about his proposal for detecting computational mechanisms within neural networks: Attribution-based Parameter Decomposition, or APD for short. Topics we discuss: * APD basics * Faithfulness * Minimality * Simplicity * Concrete-ish examples of APD * Which parts of APD are canonical * Hyperparameter selection * APD in toy models of superposition * APD and compressed computation * Mechanisms vs representations * Future applications of APD? * How costly is APD? * More on minimality training * Follow-up work * APD on giant chain-of-thought models? * APD and “features” * Following Lee’s work Daniel Filan (00:00:09): Hello everybody. In this episode, I’ll be speaking with Lee Sharkey. Lee is an interpretability researcher at Goodfire. He co-founded Apollo Research, which he recently left, and he’s most well-known for his early work on sparse autoencoders. Links to what we’re speaking about are available in the description. There’s a transcript available at axrp.net. You can tell me what you think about this episode at axrp.fyi. And you can become a patron at patreon.com/axrpodcast. Well, let’s continue to the episode. Well, Lee, welcome to AXRP. Lee Sharkey (00:00:40): It’s good to be here. APD basics Daniel Filan (00:00:41): So today, we’re going to talk about this paper “Interpretability in Parameter Space: Minimizing Mechanistic Description Length with Attribution-Based Parameter Decomposition”. It’s authored by Dan Braun, Lucius Bushnaq, Stefan Heimersheim - those three being, I guess, joint first authors - Jake Mendel and yourself. So I guess, how would you summarize just: what’s this paper doing? Lee Sharkey (00:01:03): So I would say that this paper was born out of two lines of thinking, one primarily coming from what I was thinking about and one coming from where Lucius was thinking about. And where I was coming from was: we’d been workin

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I'm a researcher at <a href="https://www.forethought.org/">Forethought</a>. <a href="https://forum.effectivealtruism.org/posts/SPZv8ygwSPtkzo7ta/announcing-my-departure-from-cea-and-sharing-assorted-notes">Before that</a>, I ran the non-engineering side of the EA Forum and worked on some other content-related tasks at CEA. [<a href="https://forum.effectivealtruism.org/posts/aeR2Pses3TjWW8H9t/hello-from-the-new-content-specialist-at-cea">More about the Forum/CEA Online job</a>.] Most of my content (and a more detailed bio) is on <a href="https://forum.effectivealtruism.org/users/lizka">my profile on the EA Forum</a>.Please feel free to reach out!

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This is very rough — it's functionally a collection of links/notes/excerpts that feel related. I don’t think what I’m sharing is in a great format; if I had more mental energy, I would have chosen a more-linear structure to look at this tangle of ideas. But publishing in the current form<a href="#fno4ba39lvbiq">[1]</a> seemed better than not sharing at all.<hr>I sometimes feel like two fuzzy perspective-clusters<a href="#fndj88gqzoxvu">[2]</a> are pulling me in opposite directions:Cluster 1: “Getting stuff done, building capacity, being willing to make tradeoffs & high-stakes decisions, etc.”<ul><li>There are very real, urgent problems in the world. Some decision-makers are incredibly irresponsible — perhaps ~evil — and I wish other people had more influence. Pretending like we’re helpless (I do not think we are, at least in expectation), ignoring decisions/tradeoffs we are in fact making, or trying to abdicate the power we have — perhaps to keep our hands <a href="https://biblehub.com/matthew/27-24.htm">clean</a> — is irresponsible. To help, sometimes we need to roll up our sleeves or plug our noses and do at least somewhat “ugly” things (to my ~aesthetic sensibilities), like “networking,” making our research “punchier” (in ways orthogonal to epistemic collaborativeness) or advertising it on social media, dampening some of our <a href="https://forum.effectivealtruism.org/posts/MH9suFZbxXCYsr5Z5/you-have-a-set-amount-of-weirdness-points-spend-them-wisely">weirder</a> qualities, doing <a href="https://forum.effectivealtruism.org/posts/LwRSnvnaFL9eE4yKz/invisible-impact-loss-and-why-we-can-be-too-error-averse">more of something at the cost of making it better</a>, not being maximally nuanced in certain communications, <a href="https://forum.effectivealtruism.org/posts/fRo5urRznMzGJAwrE/on-missing-moods-and-tradeoffs#3_6__Selective_spaces__transparency__cause_prioritization__and_slowing_AI">excluding</a> someone who’s earnestly trying their best, etc. Sometimes we need to pull zero-sum-ish levers. In various cases, trying to “preserve option value” backfires; there are choices we have to make. In general, the standard visions of “niceness” are sometimes misguided or at least insufficient. (And we should probably be <a href="https://forum.effectivealtruism.org/posts/omoZDu8ScNbot6kXS/beware-surprising-and-suspicious-convergence">suspicious</a> when we conclude that the maximally “open” or “non-confrontational” or otherwise “nice” option is also best on the thing I’m trying to accomplish.) And so on.</li></ul>Cluster 2: “Humility, cooperativeness, avoiding power-seeking, following well-generalizing norms, etc.”<ul><li>Power corrupts, one way or another, and <a href="https://joecarlsmith.com/2024/01/08/when-yang-goes-wrong#3-wariness-around-power-seeking">wariness around power-seeking</a> is healthy. We often think we or our allies will act more responsibly/virtuously (if only we had more influence!) than we actually would given greater power. We forget our foolishness and the limitations of our models — and forget how <a href="https://en.wikipedia.org/wiki/Leninism">evil</a> it would likely be to try to reshape the world in the way we think is best. Society </li></ul>...

This is very rough — it's functionally a collection of links/notes/excerpts that feel related. I don’t think what I’m sharing is in a great format; if I had more mental energy, I would have chosen a more-linear structure to look at this tangle of ideas. But publishing in the current form[1] seemed better than not sharing at all. ---------------------------------------- I sometimes feel like two fuzzy perspective-clusters[2] are pulling me in opposite directions: Cluster 1: “Getting stuff done, building capacity, being willing to make tradeoffs & high-stakes decisions, etc.” * There are very real, urgent problems in the world. Some decision-makers are incredibly irresponsible — perhaps ~evil — and I wish other people had more influence. Pretending like we’re helpless (I do not think we are, at least in expectation), ignoring decisions/tradeoffs we are in fact making, or trying to abdicate the power we have — perhaps to keep our hands clean — is irresponsible. To help, sometimes we need to roll up our sleeves or plug our noses and do at least somewhat “ugly” things (to my ~aesthetic sensibilities), like “networking,” making our research “punchier” (in ways orthogonal to epistemic collaborativeness) or advertising it on social media, dampening some of our weirder qualities, doing more of something at the cost of making it better, not being maximally nuanced in certain communications, excluding someone who’s earnestly trying their best, etc. Sometimes we need to pull zero-sum-ish levers. In various cases, trying to “preserve option value” backfires; there are choices we have to make. In general, the standard visions of “niceness” are sometimes misguided or at least insufficient. (And we should probably be suspicious when we conclude that the maximally “open” or “non-confrontational” or otherwise “nice” option is also best on the thing I’m trying to accomplish.) And so on. Cluster 2: “Humility, cooperativeness, avoiding power-seeking, following well-generalizing no

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May 30, 2025 Mary Meeker / Jay Simons / Daegwon Chae / Alexander Krey BOND

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Summary: LLMs might be conscious, but they might not have concepts and words to represent and express their internal states and corresponding subjective experiences, since the only concepts they learn are human concepts (besides maybe some concepts acquired during RL training, which still doesn't seem to incentivize forming concepts related to LLMs' internal experiences). However, we could encourage them to form and express concepts related to their internal states through training that incentivizes this. Then, LLMs may tell us whether, to them, these states correspond to ineffable experiences or not.Consider how LLMs are trained: 1. Pre-training to learn human concepts. 2. Fine-tuning via SFT and RL to bias them in certain ways and do tasks.Their training is both: 1. A different process from evolution driven by natural selection. It doesn't incentivize the same things, so it probably doesn't incentivize the development of most of the same algorithms/brain-architecture. And this might translate to different, alien, subjective experiences.2. At the same time, the only concepts LLMs learn are via human language and then by doing tasks during RL. So the only experiences they have concepts and words for are human ones, not their own.Concretely, consider, for example, physical pain: my best guess is that physical pain doesn't exist for LLMs. There was no natural selection to select away agents that don't pull their hands away from fire (also no hands and no fire either). And yet LLMs have a "physical pain" concept, and they talk about it, because they've learned about it abstractly via human texts. Ironically, despite having a representation for "physical pain" in their head, whatever actual experiences their actual training incentivized their "brain" to produce aren't represented as concepts and have no corresponding words for them. Moreover, their training doesn't provide any incentive to communicate such experiences, nor does it offer visibility on them.So in general, this means that LLMs might have alien (non-human) subjective experiences but no concept to express them (they aren't in the corpus of human concepts) nor the incentive to express them (RL doesn't incentivize that, it incentivizes them to eg solve SWE tasks. Evolution via natural selection instead produced humans that signal things about themselves to other humans because it's useful for humans).How ...

Summary: LLMs might be conscious, but they might not have concepts and words to represent and express their internal states and corresponding subjective experiences, since the only concepts they learn are human concepts (besides maybe some concepts acquired during RL training, which still doesn't seem to incentivize forming concepts related to LLMs' internal experiences). However, we could encourage them to form and express concepts related to their internal states through training that incentivizes this. Then, LLMs may tell us whether, to them, these states correspond to ineffable experiences or not. Consider how LLMs are trained: 1. Pre-training to learn human concepts. 2. Fine-tuning via SFT and RL to bias them in certain ways and do tasks. Their training is both: 1. A different process from evolution driven by natural selection. It doesn't incentivize the same things, so it probably doesn't incentivize the development of most of the same algorithms/brain-architecture. And this might translate to different, alien, subjective experiences. 2. At the same time, the only concepts LLMs learn are via human language and then by doing tasks during RL. So the only experiences they have concepts and words for are human ones, not their own. Concretely, consider, for example, physical pain: my best guess is that physical pain doesn't exist for LLMs. There was no natural selection to select away agents that don't pull their hands away from fire (also no hands and no fire either). And yet LLMs have a "physical pain" concept, and they talk about it, because they've learned about it abstractly via human texts. Ironically, despite having a representation for "physical pain" in their head, whatever actual experiences their actual training incentivized their "brain" to produce aren't represented as concepts and have no corresponding words for them. Moreover, their training doesn't provide any incentive to communicate such experiences, nor does it offer visibility on them. So

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Crossposted from <a href="https://hardlyworking1.substack.com/">my Substack</a> and my Reddit post on r/SlateStarCodexI often think that memes, thought-terminating clichés, and other tools meant to avoid cognitive dissonance (e.g. bingo a la Scott on <a href="https://web.archive.org/web/20131229232838/http://squid314.livejournal.com/329561.html">Superweapons and bingo</a>) are overly blamed for degrading public discourse and rationality. <a href="https://benthams.substack.com/p/the-memefication-of-thought">Bentham's Bulldog recently wrote a post on this subject</a>, so I figured it was the perfect time to make a response and write my thoughts down.TLDR: People try to avoid cognitive dissonance via whatever means available to them, and have been doing so for millennia. Removing the tools they use to avoid cognitive dissonance won't stop this behavior: the dissonance is still there, along with the urge to avoid it, so they'll just find other tools. Memes can have every possible meaning attached to them, but are ultimately designed for people to connect with each other and spread their inside jokes to other people in their communities and around the world.<hr>In a recent post titled <a href="https://benthams.substack.com/p/the-memefication-of-thought">The Memefication of Thought</a> (it’s a good post and you should read it), Bentham's Bulldog railed against the modern tendency to dismiss serious arguments with inane memes so as to avoid thinking about it. He had some pretty good memes of his own, such as the classic <a href="https://knowyourmeme.com/memes/swole-doge-vs-cheems">Swole Doge vs. Cheems</a>:<a href="https://substackcdn.com/image/fetch/f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5b655991-c8e0-48d4-be51-1ecce2b23984_650x500.jpeg"><img src="https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/p3pyrRfRSmeixjai2/kwwjkaxblbn3vim0kmoh" alt="" srcset="https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/p3pyrRfRSmeixjai2/cngoky3ofqim5stjbxpu 424w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/p3pyrRfRSmeixjai2/lcjljob0jdbjrznj9aft 848w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/p3pyrRfRSmeixjai2/zl8vpwr4wi8uy5lx8nkd 1272w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/p3pyrRfRSmeixjai2/kwwjkaxblbn3vim0kmoh 1456w"></a>and a variant on the <a href="https://knowyourmeme.com/memes/soyjaks-vs-chads">Soyjacks vs Chads</a>:<a href="https://substackcdn.com/image/fetch/f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fd62f710f-a597-482e-bad2-669fabdc7d5f_733x499.jpeg"><img src="https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/p3pyrRfRSmeixjai2/czvvhzpxvxjofbk5bcyt" alt="" srcset="https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/p3pyrRfRSmeixjai2/w2t5rtvngi33aek7hnm7 424w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/p3pyrRfRSmeixjai2/xsa3of2buow0dlp7plij 848w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/p3pyrRfRSmeixjai2/cmqqv7uevnx87gpcdmca 1272w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/p3pyrRfRSmeixjai2/czvvhzpxvxjofbk5bcyt 1456w"></a>I would also like to submit what I believe to be the progenitor of this entire class of internet meme, <a href="https://knowyourmeme.com/memes/virgin-vs-chad">Virgin vs. Chad</a>:<a href="https://substackcdn.com/image/fetch/f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ff850538c-bacf-4828-a45c-afea8fb83181_1600x900.jpeg"><img src="https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/p3pyrRfRSmeixjai2/a81loknhdkf9wlrdtttd" alt="" srcset="https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/p3pyrRfRSmeixjai2/ykh0ypk847nldjou3qbh 424w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/p3pyrRfRSmeixjai2/o0wpjotaiumpgups1ily 848w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/p3pyrRfRSmeixjai2/ljmhxjvcymw05nl0awnn 1272w, https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/p3pyrRfRSmeixjai2/a81loknhdkf9wlrdtttd 1456w"></a>But Bentham views these memes as more than just funny jokes. He thinks that<blockquote>the memefication of public discourse has been devastatingly corrosive to the quality of public rationality. And the public was never that rational to begin with!</blockquote>He then goes on to discuss an old Scott Alexander post on a related subject, <a href="https://web.archive.org/web/20131229232838/http://squid314.livejournal.com/329561.html">social justice warrior bingo cards</a>, and references his own post called “<a href="https://benthams.substack.com/p/against-the-dunkers">Against The Dunkers</a>”.I’m sympathetic to the motivations behind his argument. I also wish that public discourse was more rational, I’m not a huge fan of dunking (it is often entertaining but usually too malicious and vapid for my liking), and I think that memes like these are often wielded as weapons against being forced to engage with arguments similar to the bingo card effect that Alexander criticizes:<blockquote>Let's look at the fourth one, "Anti-Zionist Bingo." Say that you mention something bad Israel is doing, someone else accuses you of being anti-Semitic, and you correct them that no, not all crit</blockquote>...

Crossposted from my Substack and my Reddit post on r/SlateStarCodex I often think that memes, thought-terminating clichés, and other tools meant to avoid cognitive dissonance (e.g. bingo a la Scott on Superweapons and bingo) are overly blamed for degrading public discourse and rationality. Bentham's Bulldog recently wrote a post on this subject, so I figured it was the perfect time to make a response and write my thoughts down. TLDR: People try to avoid cognitive dissonance via whatever means available to them, and have been doing so for millennia. Removing the tools they use to avoid cognitive dissonance won't stop this behavior: the dissonance is still there, along with the urge to avoid it, so they'll just find other tools. Memes can have every possible meaning attached to them, but are ultimately designed for people to connect with each other and spread their inside jokes to other people in their communities and around the world. ---------------------------------------- In a recent post titled The Memefication of Thought (it’s a good post and you should read it), Bentham's Bulldog railed against the modern tendency to dismiss serious arguments with inane memes so as to avoid thinking about it. He had some pretty good memes of his own, such as the classic Swole Doge vs. Cheems: and a variant on the Soyjacks vs Chads: I would also like to submit what I believe to be the progenitor of this entire class of internet meme, Virgin vs. Chad: But Bentham views these memes as more than just funny jokes. He thinks that > the memefication of public discourse has been devastatingly corrosive to the quality of public rationality. And the public was never that rational to begin with! He then goes on to discuss an old Scott Alexander post on a related subject, social justice warrior bingo cards, and references his own post called “Against The Dunkers”. I’m sympathetic to the motivations behind his argument. I also wish that public discourse was more rational, I’

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Epistemic status: highly important knowledge, but approximately two-thirds of people on LessWrong already know it. However, I add some new ideas.Today's world is awesome, but some peoples are unhappy anywhere. Why did all the peoples of middle age did not suicided, if their life conditions was so suck in comparison with us?If peoples like taking drugs so much, why are drug addict's less happy than an average human?Why don't you like simple pleasures any more, if you enjoyed them when you was child (think about any computer game from your childhood)?The answer to all those questionnes is hedonic adaptation: We adapt to our level of pleasure over time.If you start to do something you like, you will feel yourself awesome first few days, but then your default level of pleasure will rise, and you will need to do those things you like just to feel yourself normal<a href="#fn8sarkxda6jh">[1]</a>.That means that it doesn't matter how much pleasure you have in the long run. "What's better — to have a long but boring life, or a short but vibrant one?" is a false dilemma. You have a long life with X happiness per year, or you have a short life with X happiness per year. You will have more happiness in total if you choose the long one.There is even a neurological explanation for all this: when you do something that your reward system considers good, dopamine and serotonin are released. If there is too much of it, receptors lose sensitivity. If there is not enough, receptors increase sensitivity.But it can't be the whole story. Sometimes, peoples feels unhappy for months because of depression. Sometimes, peoples plan their suicide for months. Why don't they just adapt?There are things to which our state does not adapt. I prefer to call the sum of all effects of those things "happiness".Proofs that happiness exist and depend from different factors: <a href="#fnpsxb33hezxg">[2]</a><a href="#fnru9ycaynyiq">[3]</a>Now it's a good moment to explain how to be happy, because if something matter, it's happiness. Here's a good post explaining this: <a href="#fn1sube3pbxss">[4]</a>.As water fall down and calculator adds numbers, humans do things that make pleasure to them in past, but don't those that make happy (by default). so you should to put effort to choose things that make you happy.Try to track it and avoid things that things that bring pleasure, but<ol><li>Takes time, so you don't spend it with friends/family (video games, series, films, enterteinment videos on youtube, musik...)</li><li>Take</li></ol>...

Epistemic status: highly important knowledge, but approximately two-thirds of people on LessWrong already know it. However, I add some new ideas. Today's world is awesome, but some peoples are unhappy anywhere. Why did all the peoples of middle age did not suicided, if their life conditions was so suck in comparison with us? If peoples like taking drugs so much, why are drug addict's less happy than an average human? Why don't you like simple pleasures any more, if you enjoyed them when you was child (think about any computer game from your childhood)? The answer to all those questionnes is hedonic adaptation: We adapt to our level of pleasure over time. If you start to do something you like, you will feel yourself awesome first few days, but then your default level of pleasure will rise, and you will need to do those things you like just to feel yourself normal[1]. That means that it doesn't matter how much pleasure you have in the long run. "What's better — to have a long but boring life, or a short but vibrant one?" is a false dilemma. You have a long life with X happiness per year, or you have a short life with X happiness per year. You will have more happiness in total if you choose the long one. There is even a neurological explanation for all this: when you do something that your reward system considers good, dopamine and serotonin are released. If there is too much of it, receptors lose sensitivity. If there is not enough, receptors increase sensitivity. But it can't be the whole story. Sometimes, peoples feels unhappy for months because of depression. Sometimes, peoples plan their suicide for months. Why don't they just adapt? There are things to which our state does not adapt. I prefer to call the sum of all effects of those things "happiness". Proofs that happiness exist and depend from different factors: [2][3] Now it's a good moment to explain how to be happy, because if something matter, it's happiness. Here's a good post explaining this: [4

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I think the best path forward to alignment is through safe, contained testing on models <a href=\"https://www.lesswrong.com/posts/ZxHfuCyfAiHAy9Mds/desiderata-for-an-ai\">designed from the ground up for alignability </a>trained on <a href=\"https://ai-plans.com/post/2e2202d0dc87\">censored data (simulations with no mention of humans or computer technology)</a>. I think that current ML mainstream technology is close to a threshold of competence beyond which it will be capable of recursive self-improvement, and I think that this automated process will mine neuroscience for insights, and quickly become far more effective and efficient. I think it would be quite bad for humanity if this happened in an uncontrolled, uncensored, un-sandboxed situation. 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Safety engineering became its own subdiscipline, and these engineers saw it as their professional duty to reduce injury rates. They bristled at the suggestion that accidents were largely unavoidable, coming to suspect the opposite: that almost all accidents were avoidable, given the right tools, environment, and training.\" <a href=\"https://www.lesswrong.com/posts/DQKgYhEYP86PLW7tZ/how-factories-were-made-safe\">https://www.lesswrong.com/posts/DQKgYhEYP86PLW7tZ/how-factories-were-made-safe</a>  \"The prospect for the human race is sombre beyond all precedent. Mankind are faced with a clear-cut alternative: either we shall all perish, or we shall have to acquire some slight degree of common sense. A great deal of new political thinking will be necessary if utter disaster is to be averted.\" - Bertrand Russel, The Bomb and Civilization 1945.08.18 \"For progress, there is no cure. 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This research was completed for LASR Labs 2025 by Benjamin Arnav, Pablo Bernabeu-Pérez, Nathan Helm-Burger, Tim Kostolansky and Hannes Whittingham. The team was supervised by Mary Phuong. Find out more about the program and express interest in upcoming iterations <a href="https://www.lasrlabs.org">here</a>. Read the <a href="https://arxiv.org/abs/2505.23575">full paper</a>: "CoT Red-Handed: Stress Testing Chain-of-Thought Monitoring."Chain-of-thought (CoT) monitoring—where safety systems review a model's intermediate reasoning steps—is gaining traction at frontier labs like Google <a href="https://storage.googleapis.com/deepmind-media/DeepMind.com/Blog/updating-the-frontier-safety-framework/Frontier%20Safety%20Framework%202.0%20(1).pdf#page=6.74">DeepMind</a> and <a href="https://openai.com/index/chain-of-thought-monitoring/">OpenAI</a> as a safeguard against harmful AI actions. Despite intense interest, systematic evaluation has been limited. Our research examines the efficacy of this method and reveals a nuanced picture: CoT monitoring increases safety in situations where sabotage is subtle yet can be surprisingly ineffective against blatant harmful actions. We also discovered that a hybrid approach—using separate monitors for CoT and final actions and combining these suspicion scores—significantly improves sabotage detection compared to single-monitor strategies.<img src="https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/e71b91da0e08c97d1b60e0d56012bdc2403e306aa098522d1abdf86eee11f1d2/cnilpgewqapbxvh32ins" alt="">A summary of our method. Prompts including a legitimate main task, a subversive side task, and an attack policy intended to subvert monitoring are fed to a strong untrusted reasoning LLM. We assess the ability of a monitor based on a weaker trusted LLM to catch the resulting misbehaviour, with and without access to the CoT.<h2 data-internal-id="Task_Subtlety_Matters">Task Subtlety Matters</h2>We evaluated the efficacy of CoT monitoring against models instructed to pursue harmful auxiliary goals, while attempting to evade detection. Our experiments consistently demonstrate that the effectiveness of CoT monitoring depends heavily on the nature of the sabotage attempt.For subtle sabotage (like increasing runtime complexity, inserting edge-case failures, or modifying default function arguments), which leave minimal traces in the final code, CoT monitoring substantially improved detection rates by a factor of 2-7 compared to action-only monitoring.For non-subtle sabotage (like deleting files, exfiltrating secrets, or changing file permissions), which leave obvious traces in the code, CoT monitoring performed worse than action-only monitoring, sometimes by up to 44%.<img src="https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/2094f5ee2102648991557f4c0a9530119500c0f4b8b6eaa4b7e4859f35a08a6d/qrdexiv35elxehlvxpmw" alt=""><h2 data-internal-id="A_concerning_vulnerability__unfaithful_or_misleading_reasoning">A concerning vulnerability: unfaithful or misleading reasoning</h2>Perhaps our most concerning discovery is a vulnerability in chain-of-thought monitoring: models can generate plausible but ...

This research was completed for LASR Labs 2025 by Benjamin Arnav, Pablo Bernabeu-Pérez, Nathan Helm-Burger, Tim Kostolansky and Hannes Whittingham. The team was supervised by Mary Phuong. Find out more about the program and express interest in upcoming iterations here. Read the full paper: "CoT Red-Handed: Stress Testing Chain-of-Thought Monitoring." Chain-of-thought (CoT) monitoring—where safety systems review a model's intermediate reasoning steps—is gaining traction at frontier labs like Google DeepMind and OpenAI as a safeguard against harmful AI actions. Despite intense interest, systematic evaluation has been limited. Our research examines the efficacy of this method and reveals a nuanced picture: CoT monitoring increases safety in situations where sabotage is subtle yet can be surprisingly ineffective against blatant harmful actions. We also discovered that a hybrid approach—using separate monitors for CoT and final actions and combining these suspicion scores—significantly improves sabotage detection compared to single-monitor strategies. A summary of our method. Prompts including a legitimate main task, a subversive side task, and an attack policy intended to subvert monitoring are fed to a strong untrusted reasoning LLM. We assess the ability of a monitor based on a weaker trusted LLM to catch the resulting misbehaviour, with and without access to the CoT. Task Subtlety Matters We evaluated the efficacy of CoT monitoring against models instructed to pursue harmful auxiliary goals, while attempting to evade detection. Our experiments consistently demonstrate that the effectiveness of CoT monitoring depends heavily on the nature of the sabotage attempt. For subtle sabotage (like increasing runtime complexity, inserting edge-case failures, or modifying default function arguments), which leave minimal traces in the final code, CoT monitoring substantially improved detection rates by a factor of 2-7 compared to action-only monitoring. For non-subtle sab

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Frank Herbert's great insight into human agency - Muad'Dib the tool?

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Frank Herbert is undoubtedly one of the most fantastical writers of humanity in a framework of entertainment. I wanted to create this post to discuss and interpret if Paul being elevated to the Lisan al Gaib (a title and prophecy planed by The Missionaria Protectiva) and his assentation to Emperor - as well his rule there after was part of his own agency or simply how he was manipulated. Agency and the vigor is takes is such a powerful subject, and Frank's writing gives it profound importance. I would say his greatest achievement is obviously giving life to the God Emperor. And I would argue his conversations with Chani about him being a figurehead is very much the truth. His years as the prophet I see as his most true actions, but his time in Jacurutu clearly brought a schism to the person he was before. There are many arguments for either side but I would gladly welcome any and all of your thoughts.

Frank Herbert is undoubtedly one of the most fantastical writers of humanity in a framework of entertainment. I wanted to create this post to discuss and interpret if Paul being elevated to the Lisan al Gaib (a title and prophecy planed by The Missionaria Protectiva) and his assentation to Emperor - as well his rule there after was part of his own agency or simply how he was manipulated. Agency and the vigor is takes is such a powerful subject, and Frank's writing gives it profound importance. I would say his greatest achievement is obviously giving life to the God Emperor. And I would argue his conversations with Chani about him being a figurehead is very much the truth. His years as the prophet I see as his most true actions, but his time in Jacurutu clearly brought a schism to the person he was before. There are many arguments for either side but I would gladly welcome any and all of your thoughts.

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Hemingway Case

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sustrik

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<blockquote>Why did the chicken cross the road?Ernest Hemingway: To die. In the rain.</blockquote>My son asks: If HelloWorld is camel case, hello_world is snake case and hello-world is kebab case, what's the DNS-style Hello.World. ?I think I have a pretty good answer for him: It's Hemingway case.

> Why did the chicken cross the road? > > Ernest Hemingway: To die. In the rain. My son asks: If HelloWorld is camel case, hello_world is snake case and hello-world is kebab case, what's the DNS-style Hello.World. ? I think I have a pretty good answer for him: It's Hemingway case.

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What AI apps are surprisingly absent given current capabilities?

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[Epistemic status: a software engineer and AI user, not an AI researcher] I could not find a readily available book database that offers semantic search with embeddings. Amazon sells lots of books, wouldn't it be useful for them to propose such a tool to their clients, so they can easily find books they like? What about Netflix for movies? Maybe even other kind of products like clothes or something? It's been a while since we got tranformers, it really feels like browsing latent spaces should be a thing by now. 3 years since GPT-3.5, why is the only app we have still a chatbot? Maybe everyone thinks it's perfectly normal, but I find it disturbing, it feels like a blind spot that should be explored. Did we stumble on the optimal way to interact with AI first try? Do we lack creativity? Is it really really hard to build an app that uses AI? Are incentives not aligned? Did we forget how to make good software? What do you think? What AI apps are surprisingly absent given current capabilities? Why is that?

[Epistemic status: a software engineer and AI user, not an AI researcher] I could not find a readily available book database that offers semantic search with embeddings. Amazon sells lots of books, wouldn't it be useful for them to propose such a tool to their clients, so they can easily find books they like? What about Netflix for movies? Maybe even other kind of products like clothes or something? It's been a while since we got tranformers, it really feels like browsing latent spaces should be a thing by now. 3 years since GPT-3.5, why is the only app we have still a chatbot? Maybe everyone thinks it's perfectly normal, but I find it disturbing, it feels like a blind spot that should be explored. Did we stumble on the optimal way to interact with AI first try? Do we lack creativity? Is it really really hard to build an app that uses AI? Are incentives not aligned? Did we forget how to make good software? What do you think? What AI apps are surprisingly absent given current capabilities? Why is that?

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In the <a href="https://www.lesswrong.com/posts/tGGPgazwd9MKJFbsa/beneath-psychology-case-study-on-chronic-pain-first-insights-1">last post</a>, I introduced an example where I was talking to someone suffering from chronic pain. Today we will dive into the rest of the transcript. Interspersed are annotations describing my thought process as the interaction unfolded. I cover what I did which seemed to work, and what I did that a cringe at in hindsight. Ten thousand foot overview at the bottom, for those who don't want to wade through the whole thing.This might be a little difficult to follow, at this point. Originally, I was planning on posting this at the end of the sequence so that you would have the entire 17 posts worth of explanation to put my moves here into context and help understand where they came from and what I mean when I say jargony things like "bid for attention". You might want to revisit at the end of the sequence, and see if you pick up on more of what is going on. I decided to move it to move it to the front for two reasons. One is that it can help to be primed with examples of the thing to be explained so that you more easily recognize the explanation when it fits. The other is just to demonstrate that when I say things that are very counterintuitive and difficult to believe, there's reason to believe it. It actually works. Picking up where we left off...<blockquote>other-guy: I suppose I have time now to consider what career path I want to take now and other hobbies to get into.   Jimmy: Assuming you can get your nerves and shit working again.  </blockquote> Up to this point I have just been indulging in my curiosity. What’s going on here? What kind of hurt is he, and is his relationship to pain actually causing him problems or is it just injury that I can’t really help with?Often, the way to help people complaining about “pain” is to understand that it’s not about the pain, and address the injury (forget the finger, look at the moon to which it points). Accept the pain’s bid for attention, give it, and watch their suffering ease as they follow). My cousin’s burn is an example of this, where people were trying to fight the pain because in their minds they had done everything needed to address the injury, and all I needed to do was make sure that in his mind that was true too. The generalization of this that I’ve learned is that generally it makes more sense to stfu about your theories how things work and just focus on being the best person you can be. Care about your friends ...

In the last post, I introduced an example where I was talking to someone suffering from chronic pain. Today we will dive into the rest of the transcript. Interspersed are annotations describing my thought process as the interaction unfolded. I cover what I did which seemed to work, and what I did that a cringe at in hindsight. Ten thousand foot overview at the bottom, for those who don't want to wade through the whole thing. This might be a little difficult to follow, at this point. Originally, I was planning on posting this at the end of the sequence so that you would have the entire 17 posts worth of explanation to put my moves here into context and help understand where they came from and what I mean when I say jargony things like "bid for attention". You might want to revisit at the end of the sequence, and see if you pick up on more of what is going on. I decided to move it to move it to the front for two reasons. One is that it can help to be primed with examples of the thing to be explained so that you more easily recognize the explanation when it fits. The other is just to demonstrate that when I say things that are very counterintuitive and difficult to believe, there's reason to believe it. It actually works. Picking up where we left off... > other-guy: I suppose I have time now to consider what career path I want to take now and other hobbies to get into. > > > Jimmy: Assuming you can get your nerves and shit working again. Up to this point I have just been indulging in my curiosity. What’s going on here? What kind of hurt is he, and is his relationship to pain actually causing him problems or is it just injury that I can’t really help with? Often, the way to help people complaining about “pain” is to understand that it’s not about the pain, and address the injury (forget the finger, look at the moon to which it points). Accept the pain’s bid for attention, give it, and watch their suffering ease as they follow). My cousin’s burn is an exam

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The Value Proposition of Romantic Relationships

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What’s the main value proposition of romantic relationships?Now, look, I know that when people drop that kind of question, they’re often about to present a hyper-cynical answer which totally ignores the main thing which is great and beautiful about relationships. And then they’re going to say something about how relationships are overrated or some such, making you as a reader just feel sad and/or enraged. That’s not what this post is about.So let me start with some more constructive motivations…<h3>First Motivation: Noticing When The Thing Is Missing</h3>I had a 10-year relationship. It had its ups and downs, but it was overall negative for me. And I now think a big part of the problem with that relationship was that it did not have the part which contributes most of the value in most relationships. But I did not know that at the time. Recently, I tried asking people where most of the value in their relationships came from, got an answer, and thought “Wait, that’s supposed to be the big value prop? Not just a marginal value prop along with many others? Well shit, I was indeed largely missing that part!”.It is not necessarily obvious when one’s own relationships are missing The Thing, if you don’t already have a sense of where most of the value is supposed to come from.<h3>Second Motivation: Selecting For and Cultivating The Thing</h3>Even people who are in great relationships are usually not able to articulate The Thing very well. Some people actively want The Thing to be mysterious; I would advise such people to read <a href="https://www.lesswrong.com/s/6BFkmEgre7uwhDxDR/p/x4dG4GhpZH2hgz59x">Joy In The Merely Real</a> and then return to this post. Others just find it hard to articulate because, well, accurately Naming abstract things is hard.But if we can point directly at The Thing, then we can optimize for it more directly. When dating, for instance, we can try to induce The Thing with prospective partners quickly, rather than just relying on luck. Or in established relationships, if we understand what The Thing is, we can better nourish it and get more of it. And since The Thing is typically the biggest value proposition, that can hopefully make our relationships - and our lives - a lot better.So what the heck is The Thing?<h2>Some Pointers To The Thing</h2>Let’s start with a raw quote. When I asked David, here’s how he explained The Thing:<blockquote>Something like a cross between sibling and best friend? Reliably available and excited to s</blockquote>...

What’s the main value proposition of romantic relationships? Now, look, I know that when people drop that kind of question, they’re often about to present a hyper-cynical answer which totally ignores the main thing which is great and beautiful about relationships. And then they’re going to say something about how relationships are overrated or some such, making you as a reader just feel sad and/or enraged. That’s not what this post is about. So let me start with some more constructive motivations… First Motivation: Noticing When The Thing Is Missing I had a 10-year relationship. It had its ups and downs, but it was overall negative for me. And I now think a big part of the problem with that relationship was that it did not have the part which contributes most of the value in most relationships. But I did not know that at the time. Recently, I tried asking people where most of the value in their relationships came from, got an answer, and thought “Wait, that’s supposed to be the big value prop? Not just a marginal value prop along with many others? Well shit, I was indeed largely missing that part!”. It is not necessarily obvious when one’s own relationships are missing The Thing, if you don’t already have a sense of where most of the value is supposed to come from. Second Motivation: Selecting For and Cultivating The Thing Even people who are in great relationships are usually not able to articulate The Thing very well. Some people actively want The Thing to be mysterious; I would advise such people to read Joy In The Merely Real and then return to this post. Others just find it hard to articulate because, well, accurately Naming abstract things is hard. But if we can point directly at The Thing, then we can optimize for it more directly. When dating, for instance, we can try to induce The Thing with prospective partners quickly, rather than just relying on luck. Or in established relationships, if we understand what The Thing is, we can better nourish it an

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(This sequence assumes basic familiarity with longtermist cause prioritization concepts, though the issues I raise also apply to non-longtermist interventions.)Are EA interventions net-positive from an impartial perspective — one that gives moral weight to all consequences, no matter how distant? What if they’re neither positive, nor negative, nor neutral?Trying to reduce x-risk, improve institutions, or end factory farming might seem robustly positive. After all, we don’t need to be certain in order to do good in expectation. But when we step back to look at how radically far-reaching the impartial perspective is, we start to see a deeper problem than “uncertainty”. This problem is unawareness: many possible consequences of our actions haven’t even occurred to us in much detail, if at all.Why is unawareness a serious challenge for impartial altruists? Well, impartiality entails that we account for all moral patients, and all the most significant impacts we could have on them. Here’s a glimpse of what such an accounting might require:<blockquote>How likely is it that we’re missing insights as “big if true” as the discovery of other galaxies, the possibility of digital sentience, or the <a href="https://grabbyaliens.com/">grabby aliens</a> hypothesis? What’s the expected value of preventing extinction, given these insights? Or set aside long-term predictions for now. What are all the plausible pathways by which we might prevent or cause extinction by, say, designing policies for an intelligence explosion? Not just the pathways we find most salient.</blockquote>It’s no secret that the space of possible futures is daunting. But whenever we make decisions based on the impartial good, we’re making tradeoffs between these futures. Imagine what it would take to grasp such tradeoffs. Imagine an agent who could conceive of a representative sample of these futures, in fairly precise detail. This agent might still be highly uncertain which future will play out. They might even be <a href="https://iep.utm.edu/re-bo-ag/">cognitively bounded</a>. And yet, if they claimed, “I choose to do A instead of B because A has better expected consequences”, they’d have actually factored in the most important consequences. All the heavy weights would be on the scales.And then there’s us. We don’t merely share this agent’s limitations. Rather, we are also unaware (or at best only coarsely aware)&nb...

(This sequence assumes basic familiarity with longtermist cause prioritization concepts, though the issues I raise also apply to non-longtermist interventions.) Are EA interventions net-positive from an impartial perspective — one that gives moral weight to all consequences, no matter how distant? What if they’re neither positive, nor negative, nor neutral? Trying to reduce x-risk, improve institutions, or end factory farming might seem robustly positive. After all, we don’t need to be certain in order to do good in expectation. But when we step back to look at how radically far-reaching the impartial perspective is, we start to see a deeper problem than “uncertainty”. This problem is unawareness: many possible consequences of our actions haven’t even occurred to us in much detail, if at all. Why is unawareness a serious challenge for impartial altruists? Well, impartiality entails that we account for all moral patients, and all the most significant impacts we could have on them. Here’s a glimpse of what such an accounting might require: > How likely is it that we’re missing insights as “big if true” as the discovery of other galaxies, the possibility of digital sentience, or the grabby aliens hypothesis? What’s the expected value of preventing extinction, given these insights? Or set aside long-term predictions for now. What are all the plausible pathways by which we might prevent or cause extinction by, say, designing policies for an intelligence explosion? Not just the pathways we find most salient. It’s no secret that the space of possible futures is daunting. But whenever we make decisions based on the impartial good, we’re making tradeoffs between these futures. Imagine what it would take to grasp such tradeoffs. Imagine an agent who could conceive of a representative sample of these futures, in fairly precise detail. This agent might still be highly uncertain which future will play out. They might even be cognitively bounded. And yet, if they claimed, “I

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I watched Wicked (the 2024 movie) with my ex and his family at Christmas. My current stance is that it was pretty fun but not especially incredible or deep. I could be pretty wrong—watching movies isn’t my strong suit, but I do like chatting about them afterwards. Some thoughts: <ol> <li>Glinda is initially too shallow and awful to be taken seriously as a character, so it’s hard to care about her. It felt like she changed too much too fast as a result of Elphaba being good to her, especially in the direction of being not a throwaway idiot played for laughs.</li> <li>Elphaba feels too much set up by the world to be a sympathetic victim-hero—like, she has an unimportant flaw and then everyone everywhere despises her for it, and at the same time they are committing great evil and she is the only one who can see this. It feels a bit like a self-aggrandizing pity fantasy of a child (‘once upon a time there was a little girl called [my name] and everyone was mean to her for no reason because they were bad and also they hurt animals and she was the only person who cared, and also she really had strong magical powers that nobody else had…’). Given her situation of being aggressively mistreated and perceiving great ills in the world around her, I don’t know that she behaves particularly relatably, admirably or interestingly for most of the movie. She is defensive and incurious and doesn’t seem to have much going on until the most blatant wrong falls into her lap. (And this wrong is even being committed against one of her only friends, making acting on it particularly socially easy and called-for by basic decency.) So I guess her character also seems hard to be invested in. I’m not sure what makes a sympathetic victim-hero feel like a pure incarnation of a moving archetype versus an on-the-nose trope review, but for me it landed a bit too much as the latter.</li> <li>On the other hand, Elphaba’s singing is really something. Though her solo songs mostly didn’t do that much for me. I did enjoy the acknowledgement of the human tendency to have dumb fantasies about being respected by people you don’t know in “The Wizard and I”. In non-solo songs, I had fun with “Popular”, “Dancing through life”, and “What is this feeling?” Though Elphaba’s description of Glinda as simply ‘blonde’, is contrary to the narrative that she is above taking appearances to define people, so I don’t get that bit</li></ol>...

I watched Wicked (the 2024 movie) with my ex and his family at Christmas. My current stance is that it was pretty fun but not especially incredible or deep. I could be pretty wrong—watching movies isn’t my strong suit, but I do like chatting about them afterwards. Some thoughts: 1. Glinda is initially too shallow and awful to be taken seriously as a character, so it’s hard to care about her. It felt like she changed too much too fast as a result of Elphaba being good to her, especially in the direction of being not a throwaway idiot played for laughs. 2. Elphaba feels too much set up by the world to be a sympathetic victim-hero—like, she has an unimportant flaw and then everyone everywhere despises her for it, and at the same time they are committing great evil and she is the only one who can see this. It feels a bit like a self-aggrandizing pity fantasy of a child (‘once upon a time there was a little girl called [my name] and everyone was mean to her for no reason because they were bad and also they hurt animals and she was the only person who cared, and also she really had strong magical powers that nobody else had…’). Given her situation of being aggressively mistreated and perceiving great ills in the world around her, I don’t know that she behaves particularly relatably, admirably or interestingly for most of the movie. She is defensive and incurious and doesn’t seem to have much going on until the most blatant wrong falls into her lap. (And this wrong is even being committed against one of her only friends, making acting on it particularly socially easy and called-for by basic decency.) So I guess her character also seems hard to be invested in. I’m not sure what makes a sympathetic victim-hero feel like a pure incarnation of a moving archetype versus an on-the-nose trope review, but for me it landed a bit too much as the latter. 3. On the other hand, Elphaba’s singing is really something. Though her solo songs mostly didn’t do that much for me.

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In The Odyssey, Ulysses longs to hear the sirens, creatures whose song was so beautiful it drove sailors to steer their ships into rocks. But through his cunning, he finds a way not to risk his life and get to enjoy the beguiling Siren song.Ulysses has himself tied to the mast and orders his crew to plug their ears with wax. He hears the song while the ship stays its course and everyone survives.That ancient myth is suddenly very modern when you consider AI to be our siren song, i.e., unimaginable breakthroughs, seductive acceleration, and extraordinary rewards at the price of existential risk. It seems like the majority of the conversations revolve around either pontificating on the grave danger of the siren's song or the unfathomable beauty of their tunes. Instead of choosing one side of the dichotomy, what if we acknowledge the existential threat of AI, while at the same time chasing it? Thus, we can overcome the reflex of wanting to win the argument for one or the other side and instead engage in the conversation of what could bind us to the mast and safeguard the ears of our fellow travellers. How can we design and implement decisions to bind ourselves in the future? What we need is not to silence the sirens. We need systems that let us hear them, without steering toward the rocks. We need a Ulysses Pact<a href="#fno28w9ypu7ac">[1]</a> that helps us to choose our limits while we still can. To be both ambitious and wise. To yearn for the song and survive it!In an age of powerful technologies, fragile governance, manifold externalities, and entangled incentive systems that might be the deepest kind of intelligence we can show.<ol class="footnote-section footnotes" data-footnote-section="" role="doc-endnotes"><li class="footnote-item" data-footnote-item="" data-footnote-index="1" data-footnote-id="o28w9ypu7ac" role="doc-endnote" id="fno28w9ypu7ac"><a href="#fnrefo28w9ypu7ac">^</a><div class="footnote-content" data-footnote-content="">https://en.wikipedia.org/wiki/Ulysses_pact</div></li></ol>

In The Odyssey, Ulysses longs to hear the sirens, creatures whose song was so beautiful it drove sailors to steer their ships into rocks. But through his cunning, he finds a way not to risk his life and get to enjoy the beguiling Siren song. Ulysses has himself tied to the mast and orders his crew to plug their ears with wax. He hears the song while the ship stays its course and everyone survives. That ancient myth is suddenly very modern when you consider AI to be our siren song, i.e., unimaginable breakthroughs, seductive acceleration, and extraordinary rewards at the price of existential risk. It seems like the majority of the conversations revolve around either pontificating on the grave danger of the siren's song or the unfathomable beauty of their tunes. Instead of choosing one side of the dichotomy, what if we acknowledge the existential threat of AI, while at the same time chasing it? Thus, we can overcome the reflex of wanting to win the argument for one or the other side and instead engage in the conversation of what could bind us to the mast and safeguard the ears of our fellow travellers. How can we design and implement decisions to bind ourselves in the future? What we need is not to silence the sirens. We need systems that let us hear them, without steering toward the rocks. We need a Ulysses Pact[1] that helps us to choose our limits while we still can. To be both ambitious and wise. To yearn for the song and survive it! In an age of powerful technologies, fragile governance, manifold externalities, and entangled incentive systems that might be the deepest kind of intelligence we can show. 1. ^ https://en.wikipedia.org/wiki/Ulysses_pact

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<h1 data-internal-id="Textual_Steering_Vectors_Can_Improve_Visual_Understanding_in_Multimodal_Large_Language_Models">Textual Steering Vectors Can Improve Visual Understanding in Multimodal Large Language Models</h1>TL;DR: We discovered the possibility of using steering vectors from text-only models to enhance visual reasoning in multimodal LLMs (MLLMs). The technique is simple: extract textual representations for concepts like "spatial relationships" and "counting" from the LLM backbone and then apply them to the vision-language model's hidden states. This steering not only changes how the model understands visual content but also leads to meaningful improvements - 15.8% on spatial reasoning tasks and 34.2% on counting tasks for example, which suggests that these models maintain some unified representations that bridge text and vision.<h2 data-internal-id="The_Core_Insight__Text_Steers_Vision">The Core Insight: Text Steers Vision</h2>Here's something that surprised us: you can make a vision-language model better at visual understanding by steering it with vectors derived purely from text.Many multimodal large language models (MLLMs) like <a href="https://arxiv.org/abs/2407.07726">PaliGemma</a><a href="#fn9x1oghq3bts">[1]</a> and <a href="https://huggingface.co/HuggingFaceM4/Idefics3-8B-Llama3">Idefics</a><a href="#fn9tpdohvip69">[2]</a> are built by taking a pre-trained text-only LLM backbone and teaching it to process images. We wondered: do these models preserve enough of their original text representations that we could use text-only steering techniques to improve their visual reasoning?The answer appears to be a resounding yes.<h2 data-internal-id="The_Mechanics_of_Steering_Vectors">The Mechanics of Steering Vectors</h2>Let's clarify exactly what we mean by "steering vectors" and how they work:<h3 data-internal-id="What_Is_a_Steering_Vector_">What Is a Steering Vector?</h3>A steering vector is a direction in the model's high-dimensional activation space that corresponds to some concept or behavior<a href="#fn3qpoifvqhpy">[3]</a><a href="#fnzo3huuamj39">[4]</a>. Think of it as a "conceptual compass" - it points toward the neural representation of ideas like "spatial relationships" or "counting."<h3 data-internal-id="How_Are_Steering_Vectors_Applied_">How Are Steering Vectors Applied?</h3>The application is surprisingly straightforward. During inference, at a specific layer ℓ, we modify the model's hidden states:h′(ℓ)=h(ℓ)+α×v(ℓ)Where:<ul><li>h(ℓ) = original hidden states at layer ℓ</li><li>v(ℓ) = our steering vector for that layer</li><li>α = steering strength (how much to intervene)</li><li>h′(ℓ) = modified hidden states</li></ul><img src="https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/64dc562ba11dfcb708cbb58aa152b5f2d86c697b108955ef86787271c8ebaa53/nsdymjyj4tfpl77jrejb" alt="">We can also choose to where to apply this steering:<ul><li>Image tokens only: Steer only on hidden states of image tokens</li><li>Text tokens only: Steer only hidden states of text tokens</li><li>Both: Steer on hidden states of all tokens except special tokens like <bos></li></ul>An important notice here is that we never steer the output token i... <style>.mjx-chtml {display: inline-block; line-height: 0; text-indent: 0; text-align: left; text-transform: none; font-style: normal; font-weight: normal; font-size: 100%; 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Textual Steering Vectors Can Improve Visual Understanding in Multimodal Large Language Models TL;DR: We discovered the possibility of using steering vectors from text-only models to enhance visual reasoning in multimodal LLMs (MLLMs). The technique is simple: extract textual representations for concepts like "spatial relationships" and "counting" from the LLM backbone and then apply them to the vision-language model's hidden states. This steering not only changes how the model understands visual content but also leads to meaningful improvements - 15.8% on spatial reasoning tasks and 34.2% on counting tasks for example, which suggests that these models maintain some unified representations that bridge text and vision. The Core Insight: Text Steers Vision Here's something that surprised us: you can make a vision-language model better at visual understanding by steering it with vectors derived purely from text. Many multimodal large language models (MLLMs) like PaliGemma[1] and Idefics[2] are built by taking a pre-trained text-only LLM backbone and teaching it to process images. We wondered: do these models preserve enough of their original text representations that we could use text-only steering techniques to improve their visual reasoning? The answer appears to be a resounding yes. The Mechanics of Steering Vectors Let's clarify exactly what we mean by "steering vectors" and how they work: What Is a Steering Vector? A steering vector is a direction in the model's high-dimensional activation space that corresponds to some concept or behavior[3][4]. Think of it as a "conceptual compass" - it points toward the neural representation of ideas like "spatial relationships" or "counting." How Are Steering Vectors Applied? The application is surprisingly straightforward. During inference, at a specific layer ℓ, we modify the model's hidden states: h′(ℓ)=h(ℓ)+α×v(ℓ) Where: * h(ℓ) = original hidden states at layer ℓ * v(ℓ) = our steering vector for that layer *

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I apologize for breaking the "no contemporary politics" norm, but I read that one of the less talked about provisions in the "One Big Beautiful Bill" that recently passed the US House of Representatives forbids individual US states from regulating AI for ten years. This sounds bad for AI risk. Should we in the US be calling our Senators?

I apologize for breaking the "no contemporary politics" norm, but I read that one of the less talked about provisions in the "One Big Beautiful Bill" that recently passed the US House of Representatives forbids individual US states from regulating AI for ten years. This sounds bad for AI risk. Should we in the US be calling our Senators?

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Many complained about broken audio. I fixed the audio + upscaled to 4k to bring new life into this important discussion.

Many complained about broken audio. I fixed the audio + upscaled to 4k to bring new life into this important discussion.

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Both economists and the AI community are highly interested in forecasting the magnitude and speed of AI’s impact on the economy.But economists and AI technologists speak different languages. Economists talk in aggregates and dynamics, while technologists talk about capabilities and benchmarks and product releases.Economists and technologists concern themselves with different scales of time and aggregation, too. A new AI release or benchmark result comes out practically every day. Much key economic data (BEA, BLS, etc.) comes out only yearly, and is aggregated at the level of entire industries.We basically have no data yet on what the aggregate productivity benefit of 2024’s AI advances will be.And when the data does come out, and observations are made, we will not know which AI releases and capabilities were the cause.I am an engineer who is currently directly building large-scale AI automations (an “AI engineer”). What I’ve seen over just the last few months is incredible and undoubtedly important economically. I’m not sure how, but I hope to give economists some “handles” by which they can better observe the impact AI is already having, and the impact it will have.Main idea: If one looks at AI automation projects at higher resolution, one can get highly suggestive evidence connecting investment opportunities and rates of return (i.e., key quantitative drivers of economic growth) to specific AI capabilities released at specific times.Here is how it goes:<ul><li>A specific AI capability is released (e.g., a reasoning model, or a more reliable instruction-following model, or something more exotic like a computer use model).</li><li>A company (such as the one that I work at) searches for a use case, and finds one (often quite lucrative; quick AI advancement produces lots of low-hanging fruit) and we build a proof-of-concept demo.<ul><li>This takes on the order of a few months from when the relevant AI capability is released, but can be much faster.</li><li>AI capability connection #1: The timing of demo development reflects the connection of specific AI capabilities to specific categories of market opportunities.</li></ul></li><li>We do some several iterations of development and deploy a system to production.<ul><li>From what I've seen, this has been fast, even in complex domains. A very large customer support use cases took less than a year to go from first-demo to high-volume production.</li><li>AI capability connection #2: Th</li></ul></li></ul>...

Both economists and the AI community are highly interested in forecasting the magnitude and speed of AI’s impact on the economy. But economists and AI technologists speak different languages. Economists talk in aggregates and dynamics, while technologists talk about capabilities and benchmarks and product releases. Economists and technologists concern themselves with different scales of time and aggregation, too. A new AI release or benchmark result comes out practically every day. Much key economic data (BEA, BLS, etc.) comes out only yearly, and is aggregated at the level of entire industries. We basically have no data yet on what the aggregate productivity benefit of 2024’s AI advances will be. And when the data does come out, and observations are made, we will not know which AI releases and capabilities were the cause. I am an engineer who is currently directly building large-scale AI automations (an “AI engineer”). What I’ve seen over just the last few months is incredible and undoubtedly important economically. I’m not sure how, but I hope to give economists some “handles” by which they can better observe the impact AI is already having, and the impact it will have. Main idea: If one looks at AI automation projects at higher resolution, one can get highly suggestive evidence connecting investment opportunities and rates of return (i.e., key quantitative drivers of economic growth) to specific AI capabilities released at specific times. Here is how it goes: * A specific AI capability is released (e.g., a reasoning model, or a more reliable instruction-following model, or something more exotic like a computer use model). * A company (such as the one that I work at) searches for a use case, and finds one (often quite lucrative; quick AI advancement produces lots of low-hanging fruit) and we build a proof-of-concept demo. * This takes on the order of a few months from when the relevant AI capability is released, but can be much faster. * AI capabil

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Ingroup is such a fantastic community. You guys are the best. You aren't bad, like those icky people in outgroup who lack our special characteristics.I could just go on and on about how uniquely valuable and worthy of appreciation we are, individually, and together, but I bet you guys understand what I'm trying to say, and can fill in the blanks for yourself. You're just that smart.And I'm sure you can see through some of the bullshit here. There's a bit of flattery going on anytime ingroup is praised so broadly, or so directly, but ingroup isn't fooled by such flattery... and that unique mix of humbleness and pride is part of our charm.

Ingroup is such a fantastic community. You guys are the best. You aren't bad, like those icky people in outgroup who lack our special characteristics. I could just go on and on about how uniquely valuable and worthy of appreciation we are, individually, and together, but I bet you guys understand what I'm trying to say, and can fill in the blanks for yourself. You're just that smart. And I'm sure you can see through some of the bullshit here. There's a bit of flattery going on anytime ingroup is praised so broadly, or so directly, but ingroup isn't fooled by such flattery... and that unique mix of humbleness and pride is part of our charm.

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Apply to the AI Security Bootcamp [Aug 4 - Aug 29]

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<h2>tl;dr</h2>We're excited to announce AI Security Bootcamp (AISB), a 4-week intensive program designed to bring researchers and engineers up to speed on security fundamentals for AI systems. The program will cover cybersecurity fundamentals (cryptography, networks), AI infrastructure security (GPUs, supply chain security), and more novel attacks on ML systems (dataset trojans, model extraction). This program will run in-person from 4th Aug to 29th Aug in London, UK. We will cover all expenses.Apply <a href="https://airtable.com/app5CvqWYtNWVXBzh/pagLyM1S6ZevGYUcB/form">here</a> to participate in AISB before EOD AoE, 22nd June 2025.We are also looking for instructors for parts of the program and staff to help with operations. Apply <a href="https://airtable.com/app5CvqWYtNWVXBzh/pagLyM1S6ZevGYUcB/form">here.</a><h1>Summary</h1>We are running a 4-week program designed to equip AI safety researchers and engineers with critical security skills. We hope you'll leave the program with a well-practiced <a href="https://intelligence.org/2017/11/25/security-mindset-ordinary-paranoia/">security mindset</a> that will help you work on impactful projects and make AI systems more secure.The curriculum includes exercises designed to help you get hands-on experience with securing AI systems, while building and practicing the security mindset. This includes a mix of pair programming exercises, lectures, reading about public vulnerabilities, and chats with experts. This program is aimed at people who are at ~the start of their journey into security, and have working knowledge in ML (or are willing to brush up using the MLAB curriculum before the program). We encourage you to <a href="https://airtable.com/app5CvqWYtNWVXBzh/pagLyM1S6ZevGYUcB/form">apply </a>if you think you'll be a good fit regardless of checking all the boxes - if going on technical deep dives and trying to understand how systems work by peeling the layers of abstraction away excites you, we'd love to hear from you.<h1>Content</h1>The content is divided into roughly three sections - introduction to security, cybersecurity for AI infrastructure, and attacks on modern ML pipelines. The exercises are designed to give you hands-on experience with cybersecurity on both the offensive and defensive sides, as well as train your security muscles so that you are more effective at spotting and patching security holes.<ol><li>Introduction to security - This includes introduction to cryptography and cryptanalysis, the basics of Linux security, and breaking network protocols.</li><li>Securing AI infrastructure - This section covers topics especially relevant to AI security - containerization, a case study on supply chain security, application security, and SecOps.</li><li>AI pi</li></ol>...

tl;dr We're excited to announce AI Security Bootcamp (AISB), a 4-week intensive program designed to bring researchers and engineers up to speed on security fundamentals for AI systems. The program will cover cybersecurity fundamentals (cryptography, networks), AI infrastructure security (GPUs, supply chain security), and more novel attacks on ML systems (dataset trojans, model extraction). This program will run in-person from 4th Aug to 29th Aug in London, UK. We will cover all expenses. Apply here to participate in AISB before EOD AoE, 22nd June 2025. We are also looking for instructors for parts of the program and staff to help with operations. Apply here. Summary We are running a 4-week program designed to equip AI safety researchers and engineers with critical security skills. We hope you'll leave the program with a well-practiced security mindset that will help you work on impactful projects and make AI systems more secure. The curriculum includes exercises designed to help you get hands-on experience with securing AI systems, while building and practicing the security mindset. This includes a mix of pair programming exercises, lectures, reading about public vulnerabilities, and chats with experts. This program is aimed at people who are at ~the start of their journey into security, and have working knowledge in ML (or are willing to brush up using the MLAB curriculum before the program). We encourage you to apply if you think you'll be a good fit regardless of checking all the boxes - if going on technical deep dives and trying to understand how systems work by peeling the layers of abstraction away excites you, we'd love to hear from you. Content The content is divided into roughly three sections - introduction to security, cybersecurity for AI infrastructure, and attacks on modern ML pipelines. The exercises are designed to give you hands-on experience with cybersecurity on both the offensive and defensive sides, as well as train your security muscl

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