Quick takes

Hey everyone, my name is Jacques, I'm an independent technical alignment researcher (primarily focused on evaluations, interpretability, and scalable oversight). I'm now focusing more of my attention on building an Alignment Research Assistant. I'm looking for people who would like to contribute to the project. This project will be private unless I say otherwise. Side note: I helped build the Alignment Research Dataset ~2 years ago. It has been used at OpenAI (by someone on the alignment team), (as far as I know) at Anthropic for evals, and is now used as the backend for Stampy.ai. If you are interested in potentially helping out (or know someone who might be!), send me a DM with a bit of your background and why you'd like to help out. To keep things focused, I may or may not accept. I have written up the vision and core features for the project here. I expect to see it evolve in terms of features, but the vision will likely remain the same. I'm currently working on some of the features and have delegated some tasks to others (tasks are in a private GitHub project board). I'm also collaborating with different groups. For now, the focus is to build core features that can be used individually but will eventually work together into the core product. In 2-3 months, I want to get it to a place where I know whether this is useful for other researchers and if we should apply for additional funding to turn it into a serious project.

We should expect that the incentives and culture for AI-focused companies to make them uniquely terrible for producing safe AGI.    From a “safety from catastrophic risk” perspective, I suspect an “AI-focused company” (e.g. Anthropic, OpenAI, Mistral) is abstractly pretty close to the worst possible organizational structure for getting us towards AGI. I have two distinct but related reasons: 1. Incentives 2. Culture From an incentives perspective, consider realistic alternative organizational structures to “AI-focused company” that nonetheless has enough firepower to host successful multibillion-dollar scientific/engineering projects: 1. As part of an intergovernmental effort (e.g. CERN’s Large Hadron Collider, the ISS) 2. As part of a governmental effort of a single country (e.g. Apollo Program, Manhattan Project, China’s Tiangong) 3. As part of a larger company (e.g. Google DeepMind, Meta AI) In each of those cases, I claim that there are stronger (though still not ideal) organizational incentives to slow down, pause/stop, or roll back deployment if there is sufficient evidence or reason to believe that further development can result in major catastrophe. In contrast, an AI-focused company has every incentive to go ahead on AI when the case for pausing is uncertain, and minimal incentive to stop or even take things slowly.  From a culture perspective, I claim that without knowing any details of the specific companies, you should expect AI-focused companies to be more likely than plausible contenders to have the following cultural elements: 1. Ideological AGI Vision AI-focused companies may have a large contingent of “true believers” who are ideologically motivated to make AGI at all costs and 2. No Pre-existing Safety Culture AI-focused companies may have minimal or no strong “safety” culture where people deeply understand, have experience in, and are motivated by a desire to avoid catastrophic outcomes.  The first one should be self-explanatory. Th

Just a prompt to say that if you've been kicking around an idea of possible relevance to the essay competition on the automation of wisdom and philosophy, now might be the moment to consider writing it up -- entries are due in three weeks.

Pretty wild discussion in this podcast about how aggressively the USSR cut corners on safety in their space program in order to stay ahead of the US. In the author's telling of the history, this was in large part because Khrushchev wanted to rack up as many "firsts" (e.g., first satellite, first woman in space) as possible. This seems like it was most proximately for prestige and propaganda rather than any immediate strategic or technological benefit (though of course the space program did eventually produce such bigger benefits). Evidence of the following claim for AI: people may not need a reason to cut corners on safety because the material benefits are so high. They may do so just because of the prestige and glory of being first. https://www.lawfaremedia.org/article/chatter--the-harrowing-history-of-the-soviet-space-program-with-john-strausbaugh

For me, perhaps the biggest takeaway from Aschenbrenner's manifesto is that even if we solve alignment, we still have an incredibly thorny coordination problem between the US and China, in which each is massively incentivized to race ahead and develop military power using superintelligence, putting them both and the rest of the world at immense risk. And I wonder if, after seeing this in advance, we can sit down and solve this coordination problem in ways that lead to a better outcome with a higher chance than the "race ahead" strategy and don't risk encountering a short period of incredibly volatile geopolitical instability in which both nations develop and possibly use never-seen-before weapons of mass destruction. Edit: although I can see how attempts at intervening in any way and raising the salience of the issue risk making the situation worse.

Besides Ilya Sutskever, is there any person not related to the EA community who quit or was fired from OpenAI for safety concerns?

OpenAI appoints Retired U.S. Army General Paul M. Nakasone to Board of Directors I don't know anything about Nakasone in particular, but it should be of interest (and concern)—especially after Situational Awareness—that OpenAI is moving itself closer to the U.S. military-industrial complex. The article itself specifically mentions Nakasone's cybersecurity experience as a benefit of having him on the board, and that he will be placed on OpenAI's board's Safety and Security Committee. None of this seems good for avoiding an arms race.

Being mindful of the incentives created by pressure campaigns I've spent the past few months trying to think about the whys and hows of large-scale public pressure campaigns (especially those targeting companies — of the sort that have been successful in animal advocacy). A high-level view of these campaigns is that they use public awareness and corporate reputation as a lever to adjust corporate incentives. But making sure that you are adjusting the right incentives is more challenging than it seems. Ironically, I think this is closely connected to specification gaming: it's often easy to accidentally incentivize companies to do more to look better, rather than doing more to be better. For example, an AI-focused campaign calling out RSPs recently began running ads that single out AI labs for speaking openly about existential risk (quoting leaders acknowledging that things could go catastrophically wrong). I can see why this is a "juicy" lever — most of the public would be pretty astonished/outraged to learn some of the beliefs that are held by AI researchers. But I'm not sure if pulling this lever is really incentivizing the right thing. As far as I can tell, AI leaders speaking openly about existential risk is good. It won't solve anything in and of itself, but it's a start — it encourages legislators and the public to take the issue seriously. In general, I think it's worth praising this when it happens. I think the same is true of implementing safety policies like RSPs, whether or not such policies are sufficient in and of themselves. If these things are used as ammunition to try to squeeze out stronger concessions, it might just incentivize the company to stop doing the good-but-inadequate thing (i.e. CEOs are less inclined to speak about the dangers of their product when it will be used as a soundbite in a campaign, and labs are probably less inclined to release good-but-inadequate safety policies when doing so creates more public backlash than they were