The authors were not anonymized, no.
Vael Gates
Karma: 1,160
This is not directly related, but I would love a way to interface with EAGs where (I pay no money, but) I have access to the Swapcard interface and I talk only with people who request meetings with me. I often want to “attend” EAGs in this way, where I don’t interface with the conference (physically or virtually) but I’m available as a resource if people want to talk to me, for which I will schedule remote 1:1s over Zoom. It’d be nice to be helpful to people at a time where they’re available and can see I’m available on Swapcard, but I don’t want to pay to do this (nor make the event organizers pay for my non-physical presence if I do end up applying—I usually want a “virtual only” ticket).
I’m not going to comment too much here, but if you haven’t seen my talk (“Researcher Perceptions of Current and Future AI” (first 48m; skip the Q&A) (Transcript)), I’d recommend it! Specifically, you want the timechunk 23m-48m in that talk, when I’m talking about the results of interviewing ~100 researchers about AI safety arguments. We’re going to publish much more on this interview data within the next month or so, but the major results are there, which describes some AI researchers cruxes.
(in response to the technical questions)
Mostly n=28 for each document, some had n =29 or n= 30; you can see details in the Appendix, quantitative section.
The Carlsmith link is to the Youtube talk version, not the full report—we chose materials based on them being pretty short.
Keeping a running list of field-building posts I personally want to keep track of:
Project ideas:
- Akash’s: https://forum.effectivealtruism.org/posts/yoP2PN5zdi4EAxdGA/ai-safety-field-building-projects-i-d-like-to-see
- Ryan’s: https://www.lesswrong.com/posts/v5z6rDuFPKM5dLpz8/probably-good-projects-for-the-ai-safety-ecosystem
Survey analysis:
- Ash’s: https://forum.effectivealtruism.org/posts/SuvMZgc4M8FziSvur/analysis-of-ai-safety-surveys-for-field-building-insights
- Daniel Filan’s: https://www.lesswrong.com/posts/rXSBvSKvKdaNkhLeJ/takeaways-from-a-survey-on-ai-alignment-resources
- https://arxiv.org/abs/2208.12852 (Bowman NLP Survey)
- AI Impacts surveys generally, 2022 and 2016, also GovAI has some
Presentation of ideas: (though this should also be updated in https://www.lesswrong.com/posts/gdyfJE3noRFSs373q/resources-i-send-to-ai-researchers-about-ai-safety )
- Marius’s: https://forum.effectivealtruism.org/posts/8JazqnCNrkJtK2Bx4/why-eas-are-skeptical-about-ai-safety
- Lukas’s: https://forum.effectivealtruism.org/posts/8JazqnCNrkJtK2Bx4/why-eas-are-skeptical-about-ai-safety
- Mine: https://forum.effectivealtruism.org/posts/q49obZkQujkYmnFWY/vael-gates-risks-from-advanced-ai-june-2022 // https://www.lesswrong.com/posts/LfHWhcfK92qh2nwku/transcripts-of-interviews-with-ai-researchers
The application form is actually really restrictive once you open it—when I filled it out, it explicitly instructed not to write any new material and only attach old material that was sent to FTXFF, and only had a <20 word box and <150 word box for grant descriptions. Today when I open the form even those boxes have disappeared. I think it’s meant to be a quite quick form, where they’ll reach out for more details later.
Just updated my post on this: https://forum.effectivealtruism.org/posts/8sAzgNcssH3mdb8ya/resources-i-send-to-ai-researchers-about-ai-safety
I have different recommendations for ML researchers / the public / proto-EAs (people are more or less skeptical to begin with, rely on different kinds of evidence, and are willing to entertain weirder or more normative hypotheses), but that post covers some introductory materials.
If they’re a somewhat skeptical ML researcher and looking for introductory material, my top recommendation at the moment is “Why I Think More NLP Researchers Should Engage with AI Safety Concerns” by Sam Bowman (2022), 15m (Note: stop at the section “The new lab”)
(quick reply to a private doc on interaction effects vs direct effects for existential risks / GCR. They’re arguing for more of a focus on interaction effects overall, I’m arguing for mostly work on direct effects. Keeping for my notes.)
In addition to direct effects from AI, bio, nuclear, climate...
...there are also mitigating / interaction effects, which could make these direct effects better or worse. For each of the possible direct risks, mitigating / interaction effects are more or less important.
For AI, the mainline direct risks that are possibly existential (whether or not the risks occur) are both really bad and also roughly independent of things that don’t reduce the risk of AGI being developed, and it’s possible to work directly on the direct risks (technical and governance). e.g. Misinformation or disinformation won’t particularly matter if we get an unaligned AGI developed at one of the big companies, except to the extent that mis/disinformation contributed to the unaligned AGI getting developed (which I think is probably more influenced by other factors, but it’s debatable), so I think efforts should be focused on solving the technical alignment problem. Misinformation and disinformation are more relevant to the governance problem, but working on them still seems worse to me than a focus on specifically trying to make AI governance go well, given the goal of trying to reduce existential risk (and not solve other important problems associated with misinformation and disinformation). (I expect one will make more progress trying to directly reduce xrisk from AI than working on related things.)
(Bio has excellent interaction effects with AI in terms of risk though, so that’s going to be one of the best examples. )
Just to quickly go over my intuitions here about the interactions:
AI x bio <-- re: AI, I think AI direct effects are worse
AI x nuclear <-- these are pretty separate problems imo
AI x climate <-- could go either way; I expect AI could have substantial improvements on climate depending on how advanced our AI gets. AI doesn’t contribute that much to climate change compared to other factors I think
Bio x AI <-- bio is SO MUCH WORSE with AI, this is an important interaction effect
Bio x nuclear <-- these are pretty separate problems imo
Bio x climate <-- worse climate will make pandemics worse for sure
Nuclear x AI <-- separate problems
Nuclear x bio <-- separate problems
Nuclear x climate <-- if climate influences war then climate makes nuclear worse
Climate x AI <-- could go either way I think but I think probably best to work directly on climate if you don’t think we’ll get advanced AI systems soon
Climate x nuclear <-- nuclear stuff certainly does mess up climate a LOT, but then we’re thinking more about nuclear
Climate x bio <-- pandemics don’t influence climate that much I think
----
Feedback from another EA (thank you!)
> I think there are more interaction effects than your shortform is implying, but also most lines of inquiry aren’t very productive. [Agree in the general direction, but object-level disagree]
I think this is true and if presented arguments I’d agree with them and have a fairer / more comprehensive picture.
Continual investment argument for why AGI will probably happen, absent major societal catastrophes, written informally, for my notes:
We’ve been working on AI since ~1950s, in an era of history that feels normal to us but in fact develops technologies very very very fast compared to most of human existence. In 2012, the deep learning revolution of AI started with AlexNet and GPUs. Deep learning has made progress even faster than the current very fast rate of progress: 10 years later, we have unprecedented and unpredicted progress in large language model systems like GPT-3, which have unusual emergent capabilities (text generation, translation, coding, math) for being trained on the next token of a language sequence. One can imagine that if we continue to pour in resources like training data and compute (as many companies are), continue to see algorithmic improvements at the rate we’ve seen, and continue to see hardware improvements (e.g. optical computing), then maybe humanity develops something like AGI or AI at very high levels of capabilities.Even if we don’t see this progress with deep learning and need a paradigm shift, there’s still an immense amount of human investment being poured into AI in terms of talent, money from private investors + government + company profits, and resources. There’s international competition to develop AI fast, there’s immense economic incentives to make AI products that make our lives ever more convenient along with other benefits, and some of the leading companies (DeepMind, OpenAI) are explicitly aiming at AGI. Given that we’ve only been working on AI since the 1950s, and the major recent progress has been in the last 10 years, and the pace of technological innovation seems very fast or accelerating with worldwide investment, it seems likely we will alive at advanced AI someday, and that someday could be well within our lifetimes, pending major societal disruption.
Thanks Gabriel—super useful step-by-step guide, and also knowledge/skill clarification structure! I usually gesture around vaguely when talking about my skills (I lose track of how much I know compared to others—the answer is I clearly completed Levels 1-3 then stopped) and trying to hire other people with related skills. It feels useful to be able to say to someone e.g. “For this position, I want you to have completed Level 1 and have a very surface level grasp of Levels 2-4”!
Still in progress as always, but this talk covers a lot of it! https://forum.effectivealtruism.org/posts/q49obZkQujkYmnFWY/vael-gates-risks-from-advanced-ai-june-2022
(Unfortunately the part about the insights isn’t transcribed—the first 20m is introduction, next ~30m is the description you want, last 10m is questions)
Suggestion for a project from Jonathan Yan:
Given the Future Fund’s recent announcement of the AI Worldview Prize, I think it would be a good idea if someone could create an online group of participants. Such a group can help participants share resources, study together, co-work, challenge each other’s views, etc. After the AI Worldview Prize competition ends, if results are good, a global AI safety community can be built and grown from there.
(“AI can have bad consequences” as a motivation for AI safety--> Yes, but AI can have bad consequences in meaningfully different ways!)
Here’s some frame confusion that I see a lot, that I think leads to confused intuitions (especially when trying to reason about existential risk from advanced AI, as opposed to today’s systems):
1. There’s (weapons) -- tech like nuclear weapons or autonomous weapons that if used correctly involve people dying. (Tech like this exists)
2. There’s (misuse) -- tech was intentioned to be anywhere from beneficial <> neutral <> seems high on offense-defense balance, but it wasn’t designed for harm. Examples here include social media, identification systems, language models, surveillance systems. (Tech like this exists)
3. There’s (advanced AI pursuing instrumental incentives --> causing existential risk), which is not about misuse, it’s about the *system itself* being an optimizer and seeking power (humans are not the problem here, the AI itself is, once the AI is sufficiently advanced). (Tech like this does not exist)
You can say “AI is bad” for all of them, and they’re all problems, but they’re different problems and should be thought of separately. (1) is a problem (autonomous weapons is the AI version of it) but is pretty independent from (3). Technical AI safety discussion is mostly about the power-seeking agent issue (3). (2) is a problem all the time for all tech (though some tech lends itself more to this than others). They’re all going to need to get solved, but at least (1) and (2) are problems humanity has any experience with (and so we have at least some structures in place to deal with them, and people are aware these are problems).
^ Yeah, endorsed! This is work in (3)-- if you’ve got the skills and interests, going to work with Josh and Lucius seems like an excellent opportunity, and they’ve got lots of interesting projects lined up.
I think my data has insights about 3, and not about 1 and 2! You can take a look at https://www.lesswrong.com/posts/LfHWhcfK92qh2nwku/transcripts-of-interviews-with-ai-researchers to see what 11 interviews look like; I think it’d have to be designed differently to get info on 1 or 2.
Sounds great; thanks Sawyer! “Reaching out to BERI” was definitely listed in my planning docs for this post; if there’s anything that seems obvious to communicate about, happy to take a call, otherwise I’ll reach out if anything seems overlapping.
Thanks levin! I realized before I published that I hadn’t gotten nearly enough governance people to review this, and indeed was hoping I’d get help in the comment section.
I’d thus be excited to hear more. Do you have specific questions / subareas of governance that are appreciably benefited by having a background in “economics, political science, legal studies, anthropology, sociology, psychology, and history” rather than a more generic “generalist”-type background (which can include any of the previous, but doesn’t depend on any of them?)
I view the core of this post as trying to push back a bit on inclusive “social scientists are useful!” framings, and instead diving into more specific instances of what kind of jobs and roles are available today that demand specific skills, or alternatively pointing out where I think background isn’t actually key and excellent generalist skills are what are sought.
“Preventing Human Extinction” at Stanford (first year undergraduate course)
Syllabus (2022)Additional subject-specific reading lists (AI, bio, nuclear, climate) (2022)
@Pablo Could you also your longtermism list with the syllabus, and with the edit that the class is taught by Steve Luby and Paul Edwards jointly? Thanks and thanks for keeping this list :) .
Great idea, thank you Vaidehi! I’m pulling this from the Forum and will repost once I get that done. (Update: Was reposted)
Nice, yeah! I wouldn’t have expected a statistically significant difference between a mean of 5.7 and 5.4 with those standard errors, but it’s nice to see it here.
I considered doing a statistical test, and then spent some time googling how to do something like a “3-paired” ANOVA on data that looks like (“s” is subject, “r” is reading):
[s1 r1 “like”] [s1 r1 “agreement”] [s1 r1 “informative”]
[s2 r1 “like”] [s2 r1 “agreement”] [s2 r1 “informative”]
… [s28 r1 “like”] [s28 r1 “agreement”] [s28 r1 “informative”]
[s1 r2 “like”] [s1 r2″agreement”] [s1 r2 “informative”]
[s2 r2 “like”] [s2 r2 “agreement”] [s2 r2 “informative”]
...
because I’d like to do an ANOVA on the raw scores, rather than the means. I did not resolve my confusion about about what to do about the 3-paired data (I guess you could lump each subject’s data in one column, or do it separately by “like”, “agreement”, and “informative”, but I’m interested in how good each of the readings are summed across the three metrics). I then gave up and just presented the summary statistics. (You can extract the raw scores from the Appendix if you put some work into it though, or I could pass along the raw scores, or you could tell me how to do this sort of analysis in Python if you wanted me to do it!)
When I look at these tables, I’m also usually squinting at the median rather than mean, though I look at both. You can see the distributions in the Appendix, which I like even better. But point taken about how it’d be nice to have stats.