As someone who gave up a career in medicine in order get a doctoral degree in Causal Inference, I am half-upvoting this, because I really want it to be true :-)
I originally trained as a medical doctor, but came to the conclusion that what I was doing had almost no value on utilitarian grounds. Sure, once in a while you feel good about helping a patient, but really, if you weren't working that day, somebody else would have done the same thing. I decided I would rather have my one-in-a-thousand chance of coming up with an original idea with real impact, instead of spending the rest of my career as a doctor, where my utilitarian impact would almost certainly be negligible.
I came to the Harvard School of Public Health intent on going into academic Global Health, but after I took an introductory course on applied causal inference with some basic DAG theory, that all changed. Partly, this was because I recognized the importance of Causal DAGs from reading Less Wrong. I ended up staying at HSPH to get a doctoral degree with some of the leading researchers in the field; this even allowed me to take a course that Ilya was a Teaching Assistant for (I ended up being a TA for the same course the following year)
Currently, my career plan is to get a faculty job at some school of public health, where I see my mission as taking part in a "reboot" of epidemiology and comparative effectiveness research, to cleanse it of the cargo cult science and magical thinking that is currently all too common, and train investigators in rigorous causal reasoning. I honestly believe that this could have a major utilitarian impact, because in the absence of randomized trials, proper causal reasoning about observational data is the only way we can learn how to make better clinical decisions that optimize patient outcomes,
( Hopefully, if I play my cards right, this career choice will also have the added benefit of giving me sufficient status in the medical community to get a real discussion started on some of the most horrific things that doctors do to patients)
Sure, once in a while you feel good about helping a patient, but really, if you weren't working that day, somebody else would have done the same thing.
Unfortunately this applies to most new math results as well (perhaps not on the same day, but eventually).
This is true, but I think a key difference is the time aspect. I am not really a causal inference researcher, I am more of a dragon slayer. The particular dragon I am engaging in battle is called cargo cult science . When fighting dragons, time is always essential; history will ask us how we allowed this dragon to terrorize us for so long. ( There are obviously more fiercesome creatures out there, but I don't really have any insight on how to defeat them, so starting with cargo cult science could at least be useful as target practice )
With this particular dragon, I believe the proper strategy is to train all scientists in causal reasoning. This is analogous to telling engineers that they can build more solid bridges if they learn calculus. The earlier you get this message out, the fewer bridges collapse. And importantly, the engineers themselves don't have to worry about the underlying mathematical theory and proofs, but it is really important that there are real mathematicians who work on that. This is why the work of people like Ilya, Pearl, Robins, Glymour, Richardson, etc is so important.
Even if you can individually help the most people by becoming a doctor, you can probably do better by paying someone with a comparative advantage at doctoring to become a doctor or do more doctoring (while you focus on what you're best at).
Never forget the power of marginal contribution. Instead of becoming a doctor you can help several people at the edge of becoming a doctor. Even if this happens only once you are already more successful than by becoming a doctor yourself.
Of course, if a lot of people are already doing this. you need to find something else to do.
This may be good advice when the socially-beneficial job in question is something less expensive than medicine. But doctors are expensive. Most of us can't afford to pay for someone else to be a doctor.
But, still, let's consider someone whose skills and interests do make it feasible for them to do so. Let's say they could work as a doctor (earning, let's say, $150k/year, and perhaps costing $200k/year "fully loaded" -- for the avoidance of doubt, all numbers here are completely made up) or as some kind of financial analyst (earning, let's say, $350k/year). After tax, perhaps our hypothetical financier is getting $240k/year, which means they could pay for a doctor and have $50k/year left over. Alternatively, they could be a doctor and take home ~$110k/year.
I repeat that all these numbers are made up (except that I checked the rough relationship between pretax and posttax income). But the overall point is pretty clear: the person we considered could pay for someone else to be a doctor, but only by taking a much better-paid job and ending up paid much less. They'd have to enjoy finance a lot more than medicine for this to be a good trade.
On the other hand, they probably can do more good by picking some job that suits them and pays well, and giving (say) 20% of their income to an effective charity. But it probably won't be (either directly or indirectly) by paying other people to be doctors.
This may be good advice when the socially-beneficial job in question is something less expensive than medicine. But doctors are expensive. Most of us can't afford to pay for someone else to be a doctor.
Doctors in Western countries are expensive. I don't think that African doctors are as expensive.
The FAI problem needs people doing math.
I think there are various biological problems where the math is underdeveloped. Physicists are as a community quite good at math but in biology there aren't that as people working on the underlying math.
Problems such as calculating cancer survival rates could benefit a lot from better statistical tools. Depending on what you mean with pure math statistics might not be included, but statistics matters.
It is impossible to predict with any certainty whether your favorite area of "pure math" will become "applied math" and when. Crypto is one standard example, and, if you believe in what MIRI is doing, then foundational/meta math like the proof theory has suddenly become applied. And once there are practical applications, the potential for "saving lives" is always there. Certainly Godel didn't think of saving lives when working out his [in]completeness theorems. So one who wants to save lives ought to do what one is best at, to maximize impact, and not worry about "saving lives" as an explicit goal. Unless, of course, you are best at translating science into saving lives.
When working on crypto I'm not sure whether you are more likely to kill people by breaking an encryption algorithm or rescue people by fixing something.
Certainly Godel didn't think of saving lives when working out his [in]completeness theorems.
I don't see how he did safe lives by working on them. Could you explain?
He hasn't yet, but if you believe that MIRI will eventually save humans from UFAI, and given that some of the basic work they do relies on his results, one can make a case for Godel inadvertently helping to save lives.
There is something to be said to improving the quality of life as well as saving lives. In scientific and discovery fields such as pure math, contributions could improve the quality of life exponentially.
Quite possibly. Do you have ideas about which math specialties and/or which problems are more likely to have a big effect?
The commenters pretty much say that he isn't, but now I'm wondering-- if you go into reasonably pure math, what areas or specific problems would be most likely to contribute the most towards saving lives?