Former management consultant and data scientist. Currently on sabbatical to try to transition to work on AI safety in some capacity.
mjkerrisonšøļø
Karma: 68
I think this dichotomy is interesting: IMO āaccountabilityā and āhealthinessā are different dimensions. Iām voting ānoā on āprotecting influential members from accountabilityā, but abstaining from āencouraging healthy accountabilityā, because while there is quite a bit of that (again IMO), thereās also unhealthy accountability at times, which (once again IMO) contributes to sometimes sparse details being published.
Oh manā¦ this really make it sound like Itās So Over
I also have this question. As someone looking to apply for funding to continue an org in the space, all of this uncertainty is tough to grapple with.
Hey NobodyInteresting,
I see youāre getting downvoted and disagreed with without any direct interaction, so Iāll bite.
diversityā¦ western developed mindsetā¦ clearly lacking
I think this is a bit combative in how itās delivered. I suspect a number of people actually agree with the āecho chamberā problemāit seems like a number of the other comments more or less say something like this in how they disagree with 80kās conclusions.
However, you might need to elaborate on the āwesternā aspect. What do you think is the shortcoming youāre identifying here? For instance, I expect a western lens is the right one for this problem because most of the frontier labs are western.
AGI delivery by 2030 will failā¦ AGI will surely be much more complex, if possible even
I think this is a controversial callāsome agree, some disagree. But I donāt think anyone can say this with confidence: for instance, the frontier lab leaders all seem to be saying this, in a way that doesnāt seem to be explained by āhypeā or āpandering to investorsā. This would be a good one to elaborate on your views /ā provide a link to the strongest evidence you buy into.
we barely have resources to properly run these current LLM models
I think this is just not true? Maybe we donāt have the resources to run them personally, but plenty of companies seem to be running their latest models for (limited) public access just fine. Are you addressing these inequality considerations or something else?
Congratulations you have now jumped on a trend-wagon that will take us nowhere, while forgetting about actual causes that are relevant right now
I think Iāve mostly responded to this, but Iād like to connect it with the āleft behindā point at the end:
I think itās good, and highly in keeping with EA principles, to (a) respond to information as it comes in and (b) think and work on the margin. This is probably too big to tackle in this commentāmaybe Iāll write something up laterābut
changing advice on the margin (for the next person) isnāt by necessity the same as āleaving people behindā
I would be interested in why or whether people who agree with the advice /ā 80kās judgment feel they should stay where they are, instead of also pivoting?
It seems to me that extinction is the ultimate form of lock-in, while surviving provides more opportunities to increase the value of the future. This moves me very far toward Agree. It seems possible, however, that there could be future that rely on actions today that are so much better than alternatives that it could be worth rolling worse dice, or futures so bad that extinction could be preferable, so this brings me back a bit from very high Agree.
On the margin: I think we are not currently well-equipped to determine whether actions are or arenāt increasing the value of the future[1]. Focusing on protecting what we have seems more prudent, as there are concerningly many concerningly high extinction risks.
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This includes things like concerns about todayās humans vs other forms of intelligence, too.
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Another tentative implication that goes without saying, but Iāll say it anyway: review who youāre listening to.
Who got these developments ārightā and āwrongā? How will you weight what those people say in the next 12 months?
Do the olā career transition into AI safety. Probably governance/āpolicy, but with a technical flavour.
(If you need stats, data science, report writing, or management consultancy skillsāplease hit me up!)
Isnāt mechinterp basically setting out to build tools for AI self-improvement?
One of the things people are most worried about is AIs recursively improving themselves. (Whether all people who claim this kind of thing as a red line will actually treat this as a red line is a separate question for another post.)
It seems to me like mechanistic interpretability is basically a really promising avenue for that. Trivial example: Claude decides that the most important thing is being the Golden Gate Bridge. Claude reads up on Anthropicās work, gets access to the relevant tools, and does brain surgery on itself to turn into Golden Gate Bridge Claude.
More meaningfully, it seems like any ability to understand in a fine-grained way whatās going on in a big model could be co-opted by an AI to ālearnā in some way. In general, I think the case that seems most likely soonest is:
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Learn in-context (e.g. results of experiments, feedback from users, things like weāve recently observed in scheming papers...)
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Translate this to appropriate adjustments to weights (identified using mechinterp research)
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Execute those adjustments
Maybe Iām late to this party and everyone was already conceptualising mechinterp as a very dual-use technology, but Iām here now.
Honestly, maybe it leans more towards āoffenseā (i.e., catastrophic misalignment) than defense! It will almost inevitably require automation to be useful, so weāre ceding it to machines out of the gate. Iād expect tomorrowās models to be better placed to make sense of and use of mechinterp techniques than humans areāpartly just because of sheer compute, but also maybe (and now Iām into speculating on stuff I understand even less) the nature of their cognition is more suited to whatās involved.
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If someone isnāt already doing so, someone should estimate what % of (self-identified?) EAs donate according to our own principles. This would be useful (1) as a heuristic for the extent to which the movement/ācommunity/āwhatever is living up to its own standards, and (1i) assuming the answer is ādecentlyā it would be useful evidence for PR/āpublicity/āresponding to marginal-faith tweets during bouts of criticism.
Looking at the Rethink survey from 2020, they have some info about which causes EAs are giving to but they seem to note that not many people respond on this? And itās not quite the same question. To do: check GWWC for whether they publish anything like this.
Edit to add: maybe an imperfect but simple and quick instrument for this could be something like āFor what fraction of your giving did you attempt a cost-effectiveness assessment (CEA), read a CEA, or rely on someone else who said they did a CEA?ā. I donāt think it actually has to be about whether the respondent got the ārightā result per se; the point is the principles. Deferring to GiveWell seems like living up to the principles because of how they make their recommendations, etc.
Can you add /ā are you comfortable adding anything on who āusā is and which orgs or what kinds of orgs are hesitant? Is your sense this is universal, or more localised (geographically, politically, cause area...)?
Good point and good fact.
My sense, though, is that if you scratch most āexpand the moral circleā statements you find a bit of implicit moral realism. I think generally thereās an unspoken ā...to be closer to its truly appropriate extentā, and that thereās an unspoken assumption that thereāll be a sensible basis for that extent. Maybe some people are making the statement prima facie though. Could make for an interesting survey.
Love to see these reports!
I have two suggestions/ārequests for ācrosstabsā on this info (which is naturally organised by evaluator, because thatās what the project is!):
As-of-today, which evaluators/ācharities sit where on the recommendation scale. The info for that is mostly on GWWCās website but not quite organised as such. Iām thinking of rows for cause areas, columns for buckets, e.g. āRecommendedā at one end and āMaybe not cost-effectiveā at the other (though maybe youād drop things off altogether). Just something to help visualise whatās moved and by how much, and broadly why are things sitting where they are (e.g. THL corporate campaigns sliding off the recommended list for āproceduralā reasons, so not in the Recommended column but now in a āNearlyā column or something).
Iād love a clear checklist of what you think needs improvement per evaluated program to help with making the list a little more evergreen. I think all that info is in your reporting, but if you called it out I think it would
help evaluated programs and
help donors to
get a sense for how up-to-date that recommendation is (given the rotating/ārolling nature of the evaluation program)
and possibly do their own assessment for whether the charity āshouldā be recommended ānowā.
Is anyone keeping tabs on where AIās actually being deployed in the wild? I feel like I mostly see (and so this could be a me problem) big-picture stuff, but there seems to be a proliferation of small actors doing weird stuff. Twitter /ā X seems to have a lot more AI content, and apparently YouTube comments do now as well (per conversation I stumbled on while watching some YouTube recreationallyālanguage & content warnings: https://āāyoutu.be/āāp068t9uc2pk?si=orES1UIoq5qTV5TH&t=2240)
I think this is a really compelling addition to EA portfolio theory. Two half-formed thoughts:
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Does portfolio theory apply better at the individual level than the community level? I think something like treating your own contributions (giving + career) as a portfolio makes a lot of sense, if youāre explicitly trying to hedge personal epistemic risk. I think this is a slightly different angle on one of Jeffās points: is this āk-level 2ā aggregate portfolio a ābetterā aggregation of everyoneās information than the āk-level 1ā³ of whatever portfolio emerges from everyone individually optimising their own portfolios? You could probably look at this analyticallyā¦ might put that on the to-do list.
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At some point what matters is specific projects...? Like when I think about āunderfundedā, Iām normally thinking thereās good projects with high expected ROI that arenāt being done, relative to some other cause area where the marginal project has a lower ROI. Maybe my point is something likeāunderfunding and accounting for it should be done at a different stage of the donation process, rather than in looking at overall what the % breakdown of the portfolio is. Maybe weāre more private equity than index fund.
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Excited to see this!
It seems useful and on priors more cost effective to centralise & outsource some of these thingsāavoiding reinventing the wheel, and producing the scale that (a) lets you build expertise and (b) makes it worthwhile investing in improvements.
I wonder if there might be particularly strong regional effects to thisāmaybe Goa had quite a large dog population, quite a lot of rabies, or quite dense dog/āhuman populations (affecting rabies, bite, and transmission incidences).
I think there could be room for further research to identify whether there would be better-looking (sub-country) regionsāthough like Helene_K found, data would be difficult.
Hey Alexanderāthanks for the write-up! I found it useful as a local, and it seems valuable to be sharing/ācoordinating on this globally.
One thing that occurred to me would be to zoom in on the sectors of the economy that are exposed to AI. I think that in Australia, it might be relatively more concentrated than elsewhereāspecifically in education, which is one of our biggest exports (though it gets accounted for domestically I think).
That could mean:
If there are distinct challenges in education vs other knowledge work, some calculus may change (not sure what exactly)
There might be stakeholders/ācoalitions we havenāt tapped yet to support less narrowly economic concerns
Rather than go through this paragraph-by-paragraph, let me pick one particular thing.
Your overall thesis is that thereās little or no evidence behind many models of AI risk.
By Ad-hoc predictive models, youāve cited a sum total of 2 blog posts criticising AI 2027 - hardly the āacademic consensusā you ask for in that paragraph.
You also donāt actually point to any specific or characteristic issues from those 2 blog posts in that paragraph, instead appealing to heuristics, concept handles, and accusations. I would honestly describe that paragraph as a narrative argument.
Overall I disagree and am also downvoting this post as not a helpful contribution.