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Long-Term Future Fund: May 2021 grant recommendations

Thanks for raising this. I think we communicated the grant decision to Po-Shen in late March/early April, when the pandemic was still significantly more active in the US. I was viewing this as "last chance to trial this idea", and I think I still stand by that given what we knew at the time, although I'd be less excited by this with the benefit of hindsight (the pandemic has developed roughly along my median expectations, but that still means I put significant credence on case rates being much higher than they currently are.)

In general our grant write-ups will appear at least a few weeks after the grant decision has actually been communicated to the grantee, as CEA needs to conduct due dilligence, we need to draft the write-up, have the write-up reviewed by the grantee, etc. For time-sensitive grants, the lag between making the grant and writing-up can be longer.

I'll also plug that the LTFF is one of the few funders that are able to make grants on short notice, so people with similarly ephemeral opportunities in the future should feel free to apply to us, there's an option in the application to flag it as time-sensitive.

How to PhD

Sorry for the (very) delayed reply here. I'll start with the most important point first.

But compared to working with a funder who, like you, wants to solve the problem and make the world be good, any of the other institutions mentioned including academia look extremely misaligned.

I think overall the incentives set up by EA funders are somewhat better than run-of-the-mill academic incentives, but I think the difference is smaller than you seem to believe, and I think we're a long way from cracking it. I think this is something we can get better at, but it's something that I expect will take significant infrastructure and iteration: e.g. new methods for peer review, experimenting with different granter-grantee relationships, etc.

Concretely, I think EA funders are really good (way better than most of academia or mainstream funders) at picking important problems like AI safety or biosecurity. I also think they're better at reasoning about possible theories of change (if this project succeeds, would it actually help?) and considering a variety of paths to impact (e.g. maybe a blog post can have more impact than a paper in this case, or maybe we'd even prefer to distribute some results privately).

However, I think most EA funders are actually worse at evaluating whether the research agenda is being executed well than the traditional academic structure. I help the LTFF evaluate grants, many of which are for independent research, and while I try to understand people's research agenda and how successful they've been, I think it's fair to say I spend at least an order of magnitude less time on this per applicant than someone's academic advisor.

Even worse, I have basically zero visibility into the process -- I only see the final write-up, and maybe have an interview with the person. If I see a negative result, it's really hard for me to tell if the person executed on the agenda well but the idea just didn't pan out, or if they bungled the process. Whereas I find it quite easy to form an opinion on projects I advise, as I can see the project evolve over time, and how the person responds to setbacks. Of course, we can (and do) ask for references, but if they're executing independently they may not have any, and there's always some CoI on advisors providing a reference.

Of course, when it comes to evaluating larger research orgs, funders can do a deeper dive and the stochasticity of research matters less (as it's averaged over a longer period of time). But this is just punting the problem to those who are running the org. In general I still think evaluating research output is a really hard problem.

I do think one huge benefit EA has is that people are mostly trying to "play fair", whereas in academia there is sadly more adversarial behavior (on the light side, people structuring their papers to dodge reviewer criticism; on the dark side, actual collusion in peer review or academic fraud). However, this isn't scalable, and I wouldn't want to build systems that rely on it.

In that more general comparison article, I think I may have still cautioned about academic incentives in particular. Because they seem, for lack of a better word, sneakier?

This is a fair point. I do think people kid themselves a bit about how much "academic freedom" they really have, and this can lead to people in effect internalizing the incentives more.

I've observed folks [...] behave as if they believe a research project to be directly good when I (and others) can't see the impact proposition, and the behavior feels best explained by publishing incentives.

Believing something is "directly good" when others disagree seems like a classic case of wishful thinking. There are lots of reasons why someone might be motivated to work on a project (despite it not, in fact, being "directly good"). Publication incentives are certainly a big one, and might well be the best explanation for the cases you saw. But in general I think it could also be that they just find that topic intellectually interesting, have been working on it for a while and are suffering from sunk cost fallacy, etc.

How to PhD

If you mean that once you are on the Junior Faculty track in CS, you don't really need to worry about well-received publications, this is interesting and doesn't line up with my models. Can you think of any examples which might help illustrate this?

To clarify, I don't think tenure is guaranteed, more that there's significant margin of error. I can't find much good data on this, but this post surveys statistics gathered from a variety of different universities, and finds anywhere between 65% of candidates get tenure (Harvard) to 90% (Cal State, UBC). Informally, my impression is that top schools in CS are the higher end of this: I'd have guessed 80%. Given this, the median person in the role could divert some of their research agenda to less well-received topics and still get tenure. But I don't think they could work on something that no one in the department or elsewhere cared about.

I've not noticed much tenure switch in CS but have never actually studied this, would love to see hard data here. I do think there's a significant difference in research agendas between junior and senior professors, but it's more a question of what was in vogue when they were in grad school and shaped their research agenda, than tenured vs non-tenured per se. I do think pre-tenure professors tend to put their students under more publication pressure though.

How to PhD

Publishing good papers is not the problem, deluding yourself is.

Big +1 to this. Doing things you don't see as a priority but which other people are excited about is fine. You can view it as kind of a trade: you work on something the research community cares about, and the research community is more likely to listen on (and work on) things you care about in the future.

But to make a difference you do eventually need to work on things that you find impactful, so you don't want to pollute your own research taste by implicitly absorbing incentives or others opinions unquestioningly.

How to PhD

You approximately can't get directly useful/ things done until you have tenure.

At least in CS, the vast majority of professors at top universities in tenure-track positions do get tenure. The hardest part is getting in. Of course all the junior professors I know work extremely hard, but I wouldn't characterize it as a publication rat race. This may not be true in other fields and outside the top universities.

The primary impediment to getting things done that I see is professors are also working as administrator and teaching, and that remains a problem post-tenure.

How to PhD

One important factor of a PhD that I don't see explicitly called out in this post is what I'd describe as "research taste": how to pick what problems to work on. I think this is one of if not the most important part of a PhD. You can only get so much faster at executing routine tasks or editing papers. But the difference between the most and mediam importance research problems can be huge.

Andrej Karpathy has a nice discussion of this:

When it comes to choosing problems you’ll hear academics talk about a mystical sense of “taste”. It’s a real thing. When you pitch a potential problem to your adviser you’ll either see their face contort, their eyes rolling, and their attention drift, or you’ll sense the excitement in their eyes as they contemplate the uncharted territory ripe for exploration. In that split second a lot happens: an evaluation of the problem’s importance, difficulty, its sexiness, its historical context (and possibly also its fit to their active grants). In other words, your adviser is likely to be a master of the outer loop and will have a highly developed sense of taste for problems. During your PhD you’ll get to acquire this sense yourself.

Clearly we might care about some of these criteria (like grants) less than others, but I think the same idea holds. I'd also recommend Chris Olah's exercises on developing research taste.

How to PhD

Thanks for writing this post, it's always useful to hear people's experiences! For others considering a PhD, I just wanted to chime in and say that my experience in a PhD program has been quite different (4th year PhD in ML at UC Berkeley). I don't know how much this is the field, program or just my personality. But I'd encourage everyone to seek a range of perspectives: PhDs are far from uniform.

I hear the point about academic incentives being bad a lot, but I don't really resonate with it. A summary of my view is that incentives are misaligned everywhere, not just academia. Rather than seeking a place with good (in general) incentives, first figure out what you want to do, and then find a place where the incentives happen to be compatible with that (even if for the "wrong" reasons).

I've worked in quant finance, industry AI labs, and academic AI research. There were serious problems with incentives in all three. I found this particularly unforgivable in quantitative finance, where the goal is pretty clear: make money. You can even measure day to day if you're making money! But getting the details right is hard. At one place I'm aware of, people were paid based on their group's profitability, divided by how risky their strategies were. This seems reasonable: profit good, risk bad. The problem was, it measured the risk of your strategy in isolation -- not how it affected the whole firm's risk levels. So different groups colluded to swap strategies, which made each of them seem less risky in isolation (so they could paid more), without changing the firm's overall strategy at all!

Incentivizing research is an unusually hard problem. Agendas can take years to pay off. The best agendas are often really high variance, so someone might fail several times but still be doing great (in expectation) work. Given this backdrop, a PhD actually seems pretty reasonable.

It's pretty hard to get fired doing a PhD, and some (by no means all) advisors will let you work on pretty much whatever you want. So, you have a 3-5 year runway to just work on whatever topics you think are best. At the end of those 3-5 years, you have to convince a panel of experts (who you get to hand-pick!) that you did something that's "worth" a PhD.

As far as things go, this is incredibly flexible, and is evidenced by the large number of people who goof of during their PhD. (This is the pitfall of weak incentives.) It also seems like a pretty reasonable incentive. If after 5 years of work you can't convince people what you did was good, it might be that it's incredibly ahead of it's time, but more likely you either need to communicate it better or the work just wasn't that great by the standards of the field.

The "by the standards of the field" is the key issue here. Some high impact work just doesn't fit well into the taste of a particular field. Perhaps it falls between disciplinary boundaries. Or it's more about distilling existing research, so isn't novel enough. That sucks, and academic research is probably the wrong venue to be pursuing this in -- but it doesn't make academic incentives bad per se. Just bad for that kind of research.

I think the bigger issue are the tacit social pressures to publish and make a name for yourself. These matter a fair bit for the job market, so it's a real pressure. But I think analogous or equal pressures exist outside of academia. If you work at an industry lab, there might be a pressure to deliver flashy results of products. If you work as an independent researcher, funders will want to see publications or other signs of progress.

I'd love to see better incentives, but I think it's important to acknowledge that mechanism design for research is a hard problem, not just that academia is screwing it up uniquely badly.

Long-Term Future Fund: Ask Us Anything!

Thanks for picking up the thread here Asya! I think I largely agree with this, especially about the competitiveness in this space. For example, with AI PhD applications, I often see extremely talented people get rejected who I'm sure would have got an offer a few years ago.

I'm pretty happy to see the LTFF offering effectively "bridge" funding for people who don't quite meet the hiring bar yet, but I think are likely to in the next few years. However, I'd be hesitant about heading towards a large fraction of people working independently long-term. I think there's huge advantages from the structure and mentorship an org can provide. If orgs aren't scaling up fast enough, then I'd prefer to focus on trying to help speed that up.

The main way I could see myself getting more excited about long-term independent research is if we saw flourishing communities forming amongst independent researchers. Efforts like LessWrong and the Alignment Forum help in terms of providing infrastructure. But right now it still seems much worse than working for an org, especially if you want to go down any of the more traditional career paths later. But I'd love to be proven wrong here.

Long-Term Future Fund: Ask Us Anything!

This is an important question. It seems like there's an implicit assumption here that highest impact path for the fund to take is to make grants which the inside view of the fund managers think is highest impact, regardless of if we can explain the grant. This is a reasonable position -- and thank you for your confidence! -- however I think the fund being legible does have some significant advantages:

  1. Accountability generally seems to improve organisations functioning. It'd be surprising if the LTFF was a complete exception to this, and legibility seems necessary for accountability.
  2. There's asymmetric information between us and donors, so less legibility will tend to mean less donations (and I think this is reasonable). So, there's a tradeoff between greater counterfactual impact from scale, v.s. greater impact per $ moved.
  3. There's may be community building value in having a fund that is attractive to people without deep context or trust in the fund managers.

I'm not sure what the right balance of legibility vs inside view is for the LTFF. One possibility would be to split into a more inside view / trust-based fund, and a more legible and "safer" fund. Then donors can choose what kind of worldview they want to buy into.

That said, personally I don't feel like I make any significantly different votes for LTFF money v.s. my own donations. The main difference would be that I am much more cautious about conflicts of interest with LTFF money than my personal money, but I don't think I'd want to change that. However, I do think I tend to have a more conservative taste in grants than some others in the long-termist community.

One thing to flag is that we do occasionally (with applicant's permission) make recommendations to private donors rather than providing funding directly from the LTFF. This is often for logistical reasons, if something is tricky for CEA to fund, but it's also an option if a grant requires a lot of context to understand (which we can provide to an individual highly engaged donor, but not in a brief public write-up). I think this further decreases the number of grant decisions that are influenced by any legibility considerations.

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