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Curated and popular this week
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Notes
The following text explores, in a speculative manner, the evolutionary question: Did high-intensity affective states, specifically Pain, emerge early in evolutionary history, or did they develop gradually over time?
Note: We are not neuroscientists; our work draws on our evolutionary biology background and our efforts to develop welfare metrics that accurately reflect reality and effectively reduce suffering. We hope these ideas may interest researchers in neuroscience, comparative cognition, and animal welfare science.
This discussion is part of a broader manuscript in progress, focusing on interspecific comparisons of affective capacities—a critical question for advancing animal welfare science and estimating the Welfare Footprint of animal-sourced products.
Key points
Ultimate question: Do primitive sentient organisms experience extreme pain intensities, or fine-grained pain intensity discrimination, or both?
Scientific framing: Pain functions as a biological signalling system that guides behavior by encoding motivational importance. The evolution of Pain signalling —its intensity range and resolution (i.e., the granularity with which differences in Pain intensity can be perceived)— can be viewed as an optimization problem, where neural architectures must balance computational efficiency, survival-driven signal prioritization, and adaptive flexibility.
Mathematical clarification: Resolution is a fundamental requirement for encoding and processing information. Pain varies not only in overall intensity but also in granularity—how finely intensity levels can be distinguished.
Hypothetical Evolutionary Pathways: by analysing affective intensity (low, high) and resolution (low, high) as independent dimensions, we describe four illustrative evolutionary scenarios that provide a structured framework to examine whether primitive sentient organisms can experience Pain of high intensity, nuanced affective intensities, both, or neither.
Introdu
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A while back (as I've just been reminded by a discussion on another thread), David Thorstad wrote a bunch of posts critiquing the idea that small reductions in extinction risk have very high value, because the expected number of people who will exist in the future is very high: https://reflectivealtruism.com/category/my-papers/mistakes-in-moral-mathematics/. The arguments are quite complicated, but the basic points are that the expected number of people in the future is much lower than longtermists estimate because:
-Longtermists tend to neglect the fact that even if your intervention blocks one extinction risk, there are others it might fail to block; surviving for billions (or more) of years likely requires driving extinction risk very low for a long period of time, and if we are not likely to survive that long, even conditional on longtermist interventions against one extinction risk succeeding, the value of preventing extinction (conditional on more happy people being valuable) is much lower.
-Longtermists tend to assume that in the future population will be roughly as large as the available resources can support. But ever since the industrial revolution, as countries get richer, their fertility rate falls and falls until it is below replacement. So we can't just assume future population sizes will be near the limits of what the available resources will support.
Thorstad goes on to argue that this weakens the case for longtermism generally, not just the value of extinction risk reductions, since the case for longtermism is that future expected population is many times the current population, or at least could be given plausible levels of longtermist extinction risk reduction effort. He also notes that if he can find multiple common mistakes in longtermist estimates of expected future population, we should expect that those estimates might be off in other ways. (At this point I would note that they could also be missing factors that bias their estimates of
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The company released a model it classified as risky — without meeting requirements it previously promised
This is the full text of a post first published on Obsolete, a Substack that I write about the intersection of capitalism, geopolitics, and artificial intelligence. I’m a freelance journalist and the author of a forthcoming book called Obsolete: Power, Profit, and the Race to Build Machine Superintelligence. Consider subscribing to stay up to date with my work.
After publication, this article was updated to include an additional response from Anthropic and to clarify that while the company's version history webpage doesn't explicitly highlight changes to the original ASL-4 commitment, discussion of these changes can be found in a redline PDF linked on that page.
Anthropic just released Claude 4 Opus, its most capable AI model to date. But in doing so, the company may have abandoned one of its earliest promises.
In September 2023, Anthropic published its Responsible Scaling Policy (RSP), a first-of-its-kind safety framework that promises to gate increasingly capable AI systems behind increasingly robust safeguards. Other leading AI companies followed suit, releasing their own versions of RSPs. The US lacks binding regulations on frontier AI systems, and these plans remain voluntary.
The core idea behind the RSP and similar frameworks is to assess AI models for dangerous capabilities, like being able to self-replicate in the wild or help novices make bioweapons. The results of these evaluations determine the risk level of the model. If the model is found to be too risky, the company commits to not releasing it until sufficient mitigation measures are in place.
Earlier today, TIME published then temporarily removed an article revealing that the yet-to-be announced Claude 4 Opus is the first Anthropic model to trigger the company's AI Safety Level 3 (ASL-3) protections, after safety evaluators found it may be able to assist novices in building bioweapons. (The
Survey Studies on Perception of EA Ideas
Has anyone looked into the possibility of doing survey studies on the perception of EA ideas? I'm thinking of surveys that might include questions that prompt the participant to choice between 2 statements. Each statement might contain an EA idea, but phrased in a different way. The goal would be to determine which verbiage is more palatable. Another type of question might measure which statement is more likely to convince the participant of a given view, or to take a certain action. The audience would be those who were not already EAs. Ideally the result would be a set of word & phrase choices that were statistically proven to be more palatable & also better at convincing people of changing their views or taking action. This set of language could then be scaled as a best practice across a wide variety of community building & fund raising efforts.
SOP for EAG Conferences
1 - clarify your goals
2- clarify types of people you’d like to have 1-1s with to meet these goals
3- pick workshops you want to go to
4- in Swapcard app, delete the 1-1 time slots that are during workshops
5- search Swapcard attendee list for relevant keywords for 1-1s
6- make 1-1s, scheduled in location where it will be easier to find ppl (ie not main networking area) — ask organizers if unsure of what this will be in advance
Notes
-don’t worry about talks since they’re recorded
-actually use 1-1 time slot feature on Swapcard (by removing times you’re not available)
—-this removes rescheduling scramble via message that otherwise occurs
-make all 1-1s in same place for your convenience
-if there’s a workshop you want to go to that’s full, try going anyway
Quantifying Impact of Allyship
Intro
Uncertainties
Goal
Why (Big/Solvable/Neglected)
The Model
Categories of Ally Action
Impact of 1 unit of action for each (weight #1)
Degree of uncertainty of 1 unit of action for each (weight #2)
Negative impact/threat
https://docs.google.com/spreadsheets/d/15QtQw1e0HNWlFzzFUidB6K0rzzoeN4YqUvYQ6tt-HvQ/edit?usp=sharing
Initial Reactions
Appendix
The factors that make a good ally
How to Train Good Allies
This reminded me of actor mapping. There's many different contexts of actor mapping, the one I originally learned about was in activism. It looks like you're trying to better tangibly quantify it, which I don't know how much exists on that. Slightly different topic, but this also reminded me of mapping a mutal aid network.
Just looked this up- very interesting. I agree that’s along the lines of what I was thinking , with the added attempt to vaguely begin to quantify . And yeah mutual aid efforts could be another type of action to include in a map/model like this.
Re how much exists - I hope it’s a lot. But I fear there may be not that much based on personal experience. Also sometimes in activist & social justice circles there can be a resistance to quantifying a bottom line.
I resist it myself haha... I was planning on getting a post out sometime about how some things just can't be quantified, with examples of math problems that are not possible to calculate. I think quantification through labels rather than numbers is really useful. I've often heard people say to solve something it must be done strategically, but it doesn't end up going through because they have a hard time conceptualizing what an effective strategy would look like.