(Cross-post from /r/EffectiveAltruism, with minor revisions.)
On the home page of 80,000 Hours, they present a key advice article outlining their primary recommendations for EA careers. According to them, this article represents the culmination of years of research and debate, and is one of the most detailed, advanced intros to EA yet.
However, while their article does go into some background ideas about the foundations of EA, one idea now stands above all else: a single, narrow focus on recruiting people to AI safety.
To be sure, the article mentions other careers. For example, the article brings up mitigation of climate change and nuclear war as potential alternatives before instantly dismissing them because they aren’t neglected. The article also briefly alludes to the other two "classic" EA cause areas, global poverty and animal welfare. However, these causes are rejected one sentence later for not focusing on the long-term. This ignores the fact that value spreading and ripple effects can affect the distant future. Quote (emphasis mine):
Some other issues we’ve focused on in the past include ending factory farming and improving health in poor countries. These areas seem especially promising if you don’t think people can or should focus on the long-term effects of their actions.
In the end, the article recommends only AI risk and biorisk as plausible EA cause areas. But even for biorisk it says,
We rate biorisk as a less pressing issue than AI safety, mainly because we think biorisks are less likely to be existential, and AI seems more likely to play a key role in shaping the long-term future in other ways.
This is a stark contrast to the effective altruism of the past, and the community as a whole that focuses on a diversity of cause areas. Now, according to 80,000 Hours, EA should focus on AI alone.
This confuses me. EA is supposed to be about evidence and practicality. Personally, I’m pretty skeptical of some of the claims that AI safety researchers have made for the priority of their work. To be clear, I do think it’s a respectable career, but is it really what we should recommend to everyone? Consider that:
- It’s not clear that advanced artificial intelligence is going to arrive any time within the next several decades. And if AI were far away it would substantially reduce EA leverage. I’m not personally that impressed by the recent deep learning revolution, which I see as essentially a bunch of brittle tools and tricks that don’t generalize well. See Gary Marcus’s critique.
- Most researchers seem to be moving away from a fast takeoff view of AI safety, and are now opting for a softer takeoff view where the effects of AI are highly distributed. If soft takeoff is true, it's much harder to see how a lot of safety work is useful. Yet, despite this shift, it seems that top EA orgs have become paradoxically more confident that artificial intelligence is cause X!
- No one really has a clear idea about what type of AI safety is useful, and one of the top AI safety organizations, MIRI, has now gone private so now we can’t even inspect whether they are doing useful work.
- Productive AI safety research work is inaccessible to over 99.9% of the population, making this advice almost useless to nearly everyone reading the article.
- Top AI safety researchers are now saying that they expect AI to be safe by default, without further intervention from EA. See here and here.
AI safety as a field should still exist, and we should still give it funding. But is it responsible for top EA organizations to make it the single cause area that trumps all others?
I’m happy to see more debate of how much we should prioritise AI safety. We intend to debate some of these issues on the podcast, and have already started recording with Ben Garfinkel.
However, I think you’re misrepresenting how much the key idea series recommends working on AI safety. We feature a range of other problem areas prominently and I don’t think many readers will come away thinking that our position is that “EA should focus on AI alone”.
We list 9 priority career paths, of which only 2 are directly related to AI safety, recommend a variety of other options, and say that there are many good options we don’t list.
Elsewhere on the page, we also discuss the importance of personal fit and coordination, which can make it better for an individual to enter different problem areas from those we most highlight.
The most relevant section is short, so I’d encourage readers of this thread to read the section and make up their own mind.
Also see this clarification of how much we focus on different causes.
Howie from 80k here.
As Ben said in his comment, the key ideas page, which is the most current summary of 80k’s views, doesn't recommend that “EA should focus on AI alone”. We don't think the EA community's focus should be anything close to that narrow.
That said, I do see how the page might give the impression that AI dominates 80k’s recommendations since most of the other paths/problems talked about are ‘meta’ or ‘capacity building’ paths. The page mentions that “we’d be excited for people to explore [our list of problems we haven’t yet investigated] as well as other areas that could foreseeably have a positive effect on the long-term future” but it doesn’t say anything about what those problems are (other than a link to our problem profiles page, which has a list).
I think it makes sense that people end up focusing on the areas we mention directly and the page could do a better job of communicating that our priorities are more diverse.
The good news is that we’re currently putting together a more thorough list of areas that we think might be very promising but aren't among our priority paths/problems. Unfortunately, it didn’t quite get done in time to add it to this version of key ideas.
More generally, I think 80k’s content was particularly heavy on AI over the last year and, while it will likely remain our top priority, I expect it will make up a smaller portion of our content over the next few years.
 Many of these will be areas we haven't yet investigated or areas that are too niche to highlight among our priority paths.
Thank you for the thoughtful response, Howie. :)
Indeed. When Todd replied earlier that only 2 of the 9 paths were directly related to AI safety, I have to say it felt slightly disingenuous to me, even though I'm sure he did not mean it that way. Many of the other paths could be interpreted as "indirectly help AI safety." (Other than that, I appreciated Todd's comment.)
I'm looking forward to this list of other potentially promising areas. Should be quite interesting.
OP's suggestion that 80k diversify the causes and careers they recommend is reasonable; I'm sure 80k can comment.
Another suggestion: Individual EAs should not defer their career decisions to 80k. People should learn from 80k's excellent advice, but ultimately they need to use their own values and understanding of their own life to make good decisions.
A friend pointed out that it would probably be good for EA community health if 80k catered to people with a wider variety of values.
Tying in a bit with Healthy Competition:
I think it makes sense (given my understanding of the folk at 80k's views) for them to focus the way they are. I expect research to go best when it follows the interests and assumptions of the researchers.
But, it seems quite reasonable if people want advice for different background assumptions to... just start doing that research, and publishing. I think career advice is a domain that can definitely benefit from having multiple people or orgs involved, just needs someone to actually step up and do it.
Note that 80k sometimes takes a softer tone, eg here:
There seems to be a large variance in researchers' estimates about timelines and takeoff-speed. Pointing to specific writeups that lean one way or another can't give much insight about the distribution of estimates. Also, I think that at least some researchers are less likely to discuss their estimates publicly if they're leaning towards shorter timelines and a discontinuous takeoff, which subjects the public discourse on the topic to a selection bias.
So I'm skeptical about the claim that "Most researchers seem to be moving away from a fast takeoff view of AI safety, and are now opting for a softer takeoff view".
Again, there seems to be a large variance in researchers' views about this. Pointing to specific writeups can't give much insight about the distribution of views.
Could you explain more about why you think people who hold those views are more likely to be silent?
Thanks for asking.
One factor that seems important is that even a small probability of "very short timelines and a sharp discontinuity" is probably a terrifying prospect for most people. Presumably, people tend to avoid saying terrifying things. Saying terrifying things can be costly, both socially and reputationally (and there's also the possible side effect of, well, making people terrified).
I hope to write a more thorough answer to this soon (I'll update this comment accordingly by 2019-11-20).
[EDIT (2019-11-18): adding the content below]
(I should note that I haven't yet discussed some of the following with anyone else. Also, so far I had very little one-on-one interaction with established AI safety researchers, so consider the following to be mere intuitions and wild speculations.)
Suppose that some AI safety researcher thinks that 'short timelines and a sharp discontinuity' is likely. Here are some potential reasons that might cause them to not discuss their estimate publicly:
Extending the point above ("people tend to avoid saying terrifying things"):
Voicing such estimates publicly might make the field of AI safety more fringe.
Some researchers might be concerned that discussing such estimates publicly would make them appear as fear mongering crooks who are just trying to get funding or better job security.
Oren Etzioni, a professor of computer science at the University of Washington and the CEO of the Allen Institute for Artificial Intelligence (not to be confused with the Alan Turing Institute) wrote an article for the MIT Technology Review in 2016 (which was summarized by an AI Impacts post on November 2019). In that article, which is titled "No, the Experts Don’t Think Superintelligent AI is a Threat to Humanity", Etzioni cited the following comment that is attributed to an anonymous AAAI Fellow:
Note: at the end of that article there's an update from November 2016 that includes the following:
See also this post by Jessica Taylor from July 2019, titled "The AI Timelines Scam" (a link post for it was posted on the EA Forum), which seems to argue for the (very reasonable) hypothesis that financial incentives have caused some people to voice short timelines estimates (it's unclear to me what fraction of that post is about AI safety orgs/people, as opposed to AI orgs/people in general).
Some researchers might be concerned that in order to explain why they have short timelines they would need to publicly point at some approaches that they think might lead to short timelines, which might draw more attention to those approaches which might cause shorter timelines in a net-negative manner.
If voicing such estimates would make some key people in industry/governments update towards shorter timelines, it might contribute to 'race dynamics'.
If a researcher with such an estimate does not see any of their peers publicly sharing such estimates, they might reason that sharing their estimate publicly is subject to the unilateralist’s curse. If the researcher has limited time or a limited network, they might opt to "play it safe", i.e. decide to not share their estimate publicly (instead of properly resolving the unilateralist’s curse by privately discussing the topic with others).
Is this the case in the AI safety community? If the reasoning for their views isn't obviously bad, I would guess that it's "cool" to say unpopular or scary but not unacceptable things, because the rationality community has been built in part on this.
I have no idea to what extent the above factor is influential amongst the AI safety community (i.e. the set of all AI safety (aspiring) researchers?).
(As an aside, I'm not sure what's the definition/boundary of the "rationality community", but obviously not all AI safety researchers are part of it.)
I'm a bit skeptical of this statement, although I admit it could be true for some people. If anything I tend to think that people have a bias for exaggerating risk rather than the opposite, although I don't have anything concrete to say either way.
Why do you think this?
EDIT: Ah, Matthew got to it first.
I think another large part of the focus comes from their views on population ethics. For example, in the article, you can "save" people by ensuring they're born in the first place:
I discuss this further in my section "Implications for EA priorities" in this post of mine. I recommend trying this tool of theirs.
"It’s not clear that advanced artificial intelligence is going to arrive any time within the next several decades" - On the other hand, it's seems, at least to me, most likely that it will. Even if several more breakthroughs would be required to reach general intelligence, those may still come relatively fast as deep learning has now finally become useful enough in a wide enough array of applications that there is far more money and talent in the field than there ever was before by orders of magnitude. Now this by itself wouldn't necessarily guarantee fast advancement in a field, but AI research is still the kind of area where a single individual can push the research forward significantly just by themselves. And governments are beginning to realise the strategic importance of AI, so even more resources are flooding the field.
"One of the top AI safety organizations, MIRI, has now gone private so now we can’t even inspect whether they are doing useful work." - this is not an unreasonable choice and we have their past record to go on. Nonetheless, there are more open options if this is important to you.
"Productive AI safety research work is inaccessible to over 99.9% of the population, making this advice almost useless to nearly everyone reading the article." - Not necessarily. Even if becoming good enough to be a researcher is very hard, it probably isn't nearly as hard to become good enough at a particular area to help mentor other people.