The signalling aspect is probably overwhelmingly important for career success and ultimately alignment. A high GPA will open up roles that you could otherwise not access. Unfortunately hiring processes often filter based on it. Top 100 is likely not good enough to merely rely on brand recognition.
(I don’t work in this field but in a competitive & technical area and I’m sure that the recent graduate CVs I see are already pre-filtered by HR based on these kinds of blunt metrics.)
Assume this is talking about majoring in Computer Science. Do you think getting high GPA (in CS major) is highly, moderately or only slightly aligned with improving AI risks research(both conceptual and emprirical) abilities? Some said they think a lot of college courses are not so useful[1], and getting good grades on tests requires practcing irrelevant skills and cramming stuffs(such as Brain Tomasik writing:"Drawbacks to Formal Education"[2]).
However, some also think most things taught in CS courses are valuable, rigorous training is beneficial to thinking skills(even if the knowledge in course itslef is not related to AI risks). I don't know what theory is more correct. Do you think chasing for great GPA(define it as getting top 20% GPA in a top 100 university) is itself benefial, or is it just mostly for credentials and signaling?
[1]https://forum.effectivealtruism.org/posts/hgiLaE3eL76ovcfdH/many-undergrads-should-take-light-courseloads
[2]https://briantomasik.com/drawbacks-formal-education/
I’d say it’s only slightly aligned with improving AI risk research abilities, but it’s moderate-to-highly aligned with career success in general.
The main benefit to a very high GPA (top 5-10%) is as a signal that you’re smart and diligent. If you’re going to a non-brand-name school, that can be useful for putting you within the same league as the applicants from brand-name schools for internships and job opportunities, including for AI risk research.
A secondary benefit is exercising the muscle of diligence and hard work. However, there are other ways you can do this which are more direct aligned with AI risk work, for example independent research, taking AI risk courses, etc.
The benefit to your relevant research abilities would only come from courses with practically relevant course material, like applied ML. I would definitely encourage you to give it your all on those courses. However, while theoretical CS is super cool, knowing it won’t matter for AI risk work. But as Tomasik and others write, college is deeply inefficient for learning, and with the proper effort you can easily learn way more than your undergrad degree will teach you by diligently gathering knowledge available online for free.
Edit: I’d also consider trying to graduate a year or two early if possible. You’ll learn way more by actually working in AI risk, it’s better on shorter timelines, you’ll get paid for time you otherwise would have spent paying for irrelevant courses, and it’s an even stronger signal of intelligence and diligence.
Hello Ariel: Thanks very much for your response, it's very helpful.
However, for my situation it's a bit different: If I double major in dentistry and CS(I'll probably do them in different colleges, the below is just discussing on the CS part). I could choose a top college in Taiwan. However, in the top college I'd have to invest like much more time in order to get a top 10-20% GPA(like 3.8). In applying master in USA, it seems GPA matters much more than your college name(especially because they don't know how big the difference of students intelligence is between the top-teir and mid-teir school in Taiwan.) Therefore, there's a case for choosing mid-teir school in order to spend less time and maintain high GPA.
So the choice may be like: 1.spend 1000 hours extra on chasing GPA, and get a 3.2 GPA in a top-tier school 2.spend 1000 hours extra on chasing GPA, get a 3.8 GPA in a mid-tier school 3.spend 2000 hours extra on chasing GPA, get a 3.8 GPA in a top-tier school. It seems obvious that 1 is the worst for applying master degree in USA, so the choice is between 2 and 3, whether it's worth investing like 1000 hours to improve your "college school name" on the resume. What do you think?
(And of course, there are still other advantages of choosing a top-tier school, like better peers and research environment.)
Happy to help! I agree with your framing of the problem.
I asked Claude which Taiwanese universities US masters' programs are most likely to have heard of. Claude says NTU has the strongest name recognition, followed by NCKU, NTHU, NCTU. Others aren't as well known internationally. If that's accurate, that tells me that if the top option you're considering is not one of those four, (2) mid tier + high GPA would be the best option.
If you're planning on working on AI risk, my understanding is that most of these companies are US or Europe-based. These companies will have even lower knowledge of the distinctions between Taiwanese universities. Unless your hiring manager or interviewer is Asian, you'll likely only get a boost from NTU and probably no benefit from NCKU, NTHU, NCTU. That leans me more in favor of (2). (Speaking as someone who interviews candidates and contributes to hiring decisions, I had dim recognition of NTU and no recognition of NCKU, NTHU, NCTU prior to writing this comment, but I expect my Asian colleagues would.)
Based on this, unless your top option is NTU, (2) mid tier + high GPA is likely best. But do your own research and don't just take my word for it :)