Trying to make sure the development of powerful artificial intelligence goes well.
RomanHauksson
Karma: 213
I agree that GHW is an excellent introduction to effectiveness and we should watch out for the practical limitations of going too meta, but I want to flag that seeing GHW as a pipeline to animal welfare and longtermism is problematic, both from a common-sense / moral uncertainty view (it feels deceitful and that’s something to avoid for its own sake) and a long-run strategic consequentialist view (I think the EA community would last longer and look better if it focused on being transparent, honest, and upfront about what most members care about, and it’s really important for the long term future of society that the core EA principles don’t die).
How about “early-start EA (EEA)”? As a term, could sit neatly beside “highly-engaged EA (HEA)”.
Sorry, I never got around to this. If someone wants to take this up, feel free!
https://www.lesswrong.com/posts/bkfgTSHhm3mqxgTmw/loudly-give-up-don-t-quietly-fade
Where can I find thoughtful research on the relationship between AI safety regulation and the centralization of power?
This is excellent research! The quality of Rethink Priorities’ output consistently impresses me.
A couple questions:
What software did you use to create figure 1?
What made you decide to use discrete periods in your model as opposed to a continuous risk probability distribution?
I agree that my answer isn’t very useful by itself, without any object-level explanations, but I do think it is useful to bring up the anthropic principle if it hasn’t been mentioned already. In hindsight, I think my answer comes off as unnecessarily dismissive.
Isn’t the opposite end of the p(doom)–longtermism quadrant also relevant? E.g. my p(doom) is 2%, but I take the arguments for longtermism seriously and think that’s high enough of a chance to justify working on the alignment problem.
80,000 Hours had an article with advice for new college students, and a section towards the end touches on your question.
Make sure to check out OpenPhil’s undergraduate scholarship if you haven’t yet.
Here are a couple of excerpts from relevant comments from the Astral Codex Ten post about the tournament. From the anecdotes, it seems as though this tournament had some flaws in execution, namely that the “superforcasters” weren’t all that. But I want to see more context if anyone has it.
I signed up for this tournament (I think? My emails related to a Hybrid Forecasting-Persuasion tournament that at the very least shares many authors), was selected, and partially participated. I found this tournament from it being referenced on ACX and am not an academic, superforecaster, or in any way involved or qualified whatsoever. I got the Stage 1 email on June 15.
I participated and AIUI got counted as a superforecaster, but I’m really not. There was one guy in my group (I don’t know what happened in other groups) who said X-risk can’t happen unless God decides to end the world. And in general the discourse was barely above “normal Internet person” level, and only about a third of us even participated in said discourse. Like I said, haven’t read the full paper so there might have been some technique to fix this, but overall I wasn’t impressed.
- 's comment on Timelines are short, p(doom) is high: a global stop to frontier AI development until x-safety consensus is our only reasonable hope by (12 Oct 2023 15:03 UTC; 4 points)
- 's comment on What do XPT forecasts tell us about AI risk? by (24 Jul 2023 9:32 UTC; 3 points)
- 's comment on What do XPT forecasts tell us about AI risk? by (13 Mar 2024 23:12 UTC; 1 point)
Same reason we haven’t been destroyed by a nuclear apocalypse yet: if we had, we wouldn’t be here talking about it.
As for the question “why haven’t we encountered a power-seeking AGI from elsewhere in the universe who didn’t destroy us”, I don’t know.
I can look into how to set up a torrent link tomorrow and let you know how it goes!
Can we set up a torrent link for this?
Rational Animations is probably the YouTube channel the report is referring to, in case anyone’s curious.
Where did you copy the quote from?
Suggestion: use a well-designed voting system such as STAR voting, approval voting, or quadratic voting.
I plan to do some self-studying in my free time over the summer, on topics I would describe as “most useful to know in the pursuit of making the technological singularity go well”. Obviously, this includes technical topics within AI alignment, but I’ve been itching to learn a broad range of subjects to make better decisions about, for example, what position I should work in to have the most counterfactual impact or what research agendas are most promising. I believe this is important because I aim to eventually attempt something really ambitious like founding an organization, which would require especially good judgement and generalist knowledge. What advice do you have on prioritizing topics to self-study and for how much depth? Any other thoughts or resources about my endeavor? I would be super grateful to have a call with you if this is something you’ve thought a lot about (Calendly link). More context: I’m a undergraduate sophomore studying Computer Science.
So far, my ordered list includes:
Productivity
Learning itself
Rationality and decision making
Epistemology
Philosophy of science
Political theory, game theory, mechanism design, artificial intelligence, philosophy of mind, analytic philosophy, forecasting, economics, neuroscience, history, psychology...
...and it’s at this point that I realize I’ve set my sights too high and I need to reach out for advice on how to prioritize subjects to learn!
EA group house?
Tech startup incubator?
Research bootcamp, e.g. MATS?