How Should We Use Limited Time to Maximize Long-Term Impact?

post by queelius · 2024-10-12T20:02:46.801Z · LW · GW · No comments

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I've been reflecting on how researchers—particularly those with limited time or resources—can best contribute to influencing the long-term future.

Assumption: Individually, we may not have the resources to fund large projects, but we still want to make contributions that move the needle, however slightly.

I approach this question with epistemic humility—recognizing that predicting long-term impact is incredibly difficult—and with humility about our own limitations. I am personally guided by two key principles:

Much has already been impressively written about these topics, and I’m particularly interested in mitigating s-risks, since reducing the chance of such suffering seems more urgent than pursuing joy-maximizing goals. However, many of us—whether due to health, time, or other constraints—are left wondering how to allocate our remaining time wisely.

My personal context: I have stage 4 cancer, so statistically, my time may be more limited than most. I’m a PhD student in Computer Science with a strong background in math (Masters). Given my situation, I’m especially interested in how those of us with limited time or resources can still make non-negligible contributions to reducing s-risks and positively shaping the long-term future.

What I’m asking for: How should we, as researchers or contributors with limited time, focus our efforts to maximize our positive impact? Are there specific areas of research or actions that seem particularly high-leverage for reducing future suffering on an astronomical scale?

Finally, I’m aware that this topic has been discussed extensively, and I’d be grateful for any links to relevant resources or existing discussions that cover this ground. My goal is not to waste anyone's time, as I’m not deeply well-read on the full body of literature. Any guidance, new or old, would be greatly appreciated.

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answer by mishka · 2024-10-13T03:11:11.533Z · LW(p) · GW(p)

This depends on many things (one's skills, one's circumstances, one's preferences and inclinations (the efficiency of one's contributions greatly depends on one's preferences and inclinations)).

I have stage 4 cancer, so statistically, my time may be more limited than most. I’m a PhD student in Computer Science with a strong background in math (Masters).

In your case, there are several strong arguments for you to focus on research efforts which can improve your chances of curing it (or, at least, of being able to maintain the situation for a long time), and a couple of (medium strength?) arguments against this choice.

For:

  • If you succeed, you'll have more time to make impact (and so if your chance of success is not too small, this will contribute to your ability to maximize your overall impact, statistically speaking).

  • Of course, any success here will imply a lot of publicly valuable impact (there are plenty of people in a similar position health-wise, and they badly need progress to occur ASAP).

  • The rapid development of applied AI models (both general purpose models and biology-specific models) creates new opportunities to datamine and juxtapose a variety of potentially relevant information and to uncover new connections which might lead to effective solutions. Our tools progress so fast that people are slow to adapt their thinking and methods to that progress. So new people with fresh outlook have reasonable shots (of course, they should aim for collaborations). In this sense, your PhD CS studies and your strong math is very helpful (a lot of the relevant models are dynamic systems, timing of interventions is typically not managed correctly as far as I know (there are plenty of ways to be nice to particularly vulnerable tissues by timing the chemo right and thus being able to make it more effective, but this is not a part of the standard-of-care yet as far as I know), and so on).

  • You are likely to be strongly motivated and to be able to maintain strong motivation. At the same time you'll know that it is the result that counts here, not the effort, and so you will be likely to try your best to approach this in a smart way, not in a brute force effort way.

Possibly against:

(Of course, there are plenty of other interesting things one can do with this background (PhD CS studies and strong math). For example, one might decide to disregard the health situation and to dive into technical aspects of AI development and AI existential safety issues, especially if one's estimate of AI timelines yields really short timelines.)

↑ comment by queelius · 2024-10-13T05:18:03.404Z · LW(p) · GW(p)

Thank you, mishka, for your thoughtful response. You’ve given me a lot to chew on, particularly regarding the potential of focusing on chemotherapy treatment timing. While I’ve explored AI-driven health research, I hadn't fully appreciated how important treatment timing, diet, exercise, and other factors may be for people in my situation.

There’s a mountain of data in this area, and using AI to predict salient data could potentially lead to improvements in how we approach chemotherapy. This seems like a practical and timely research direction, assuming it is still somewhat niche.

I appreciate your input.

Replies from: mishka
↑ comment by mishka · 2024-10-13T05:34:38.319Z · LW(p) · GW(p)

the potential of focusing on chemotherapy treatment timing

More concretely (this is someone's else old idea), what I think is still not done is the following. Chemo kills dividing cells, this is why the rapidly renewing tissues and cell populations are particularly vulnerable.

If one wants to spare one of those cell types (say, a particular population of immune cells), one should take the typical period of its renewal, and use that as a period of chemo sessions (time between chemo sessions, a "resonance" of sorts between that and the period of the cell population renewal for the selected cell type). Then one should expect to spare most of that population (and might potentially be able to use higher doses for better effect, if the spared population is the most critical one; this does need some precision, not a typical today's "relaxed logistics" approach where a few days this or that way in the schedule is nothing to worry about).

I don't know if that ever progressed beyond the initial idea...

(That's just one example, of course, there is a lot of things which can be considered and, perhaps, tried.)

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