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TL;DR: Is it possible to pursue applied research in AI and theoretical physics after a PhD degree in CS focusing on applied AI (as a professor at some universities)?

Long version:

Hello, I'm a 3rd year undergrad at a university in Vietnam.

I've been wondering about the practicality of the above questions for quite some time but I have not yet been able to decide.

For a little bit of background:

I have a fairly good foundation on CS because I have been doing competitive programming since I was 13. Until recently, I feel that research in AI was too applied (I did some research with an advisor on computer vision recently), and then, I discovered theoretical physics and got the feeling that "This is for me, my true self".

However, the problem is, in my country, pursuing theoretical research does not really guarantee a stable future career (with good money). So, I'm thinking about doing a PhD in CS focusing on applied AI and self-study physics. After that, I will probably get a position at a university and conduct research in both direction (more focus is put on only one direction, of course).

Can you kindly provide me some pieces of advice for my future career path, or, is my plan possible?

Thank you for spending time reading and answering my question.

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    Sure, it is possible. Whether you will be successful, is another matter. You do well by pursuing what attracts/interest you. However, given that you are limited by your environment, then there is always sacrifice. You could leave Vietnam and pursue your interest in another country, or stay, and pursue a more lucrative topic that is not your primary interest. Alternatively, you could marry the two areas; e.g., quantum computing. All the best! – Prof. Santa Claus Oct 3 at 3:11
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    You could study physics and move to AI later. Many top AI researchers were originally physicists. It's a very good education, and with your programming background, you will have possibilities. Self-studying physics is difficult, because there is a lot nonobvious stuff that you have to pick up from your profs and peers. Programming has a lot more hands-on resources, and - so to say - via running software, you can do your own experiments. That possibility is very limited in physics. TL;DR: studying physics, moving to AI is - in my opinion - more viable than the other way round. – Captain Emacs Oct 3 at 4:15
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    Are you interested in / willing to combine them? I do not know about theoretical physics specifically, but there is plenty of room for overlap between AI and physics. – cag51 Oct 3 at 4:15
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    @Captain Emacs: I'm also aware of the difficulty of physics, too. But in my current situation, I think taking a PhD in CS is easier since a PhD means I proved my ability to do independent research, not about how much I know about CS/AI (from my advisor). So, I'm considering trying to make connections with other physics students in Vietnam to study with them, and I think I can move to physics after completing the PhD first. – Cuong Nguyen Oct 3 at 4:29
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    There has been interesting work (dating back to the 1990s) in using genetic programming to have computers rediscover Kepler's laws of planetary motion (see this for a nontechnical discussion). Surely there is more work that could be done along similar lines. If you have some freedom in your dissertation topic, you could pick one that involves using AI in a problem in physics. – John Coleman Oct 4 at 12:20
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It is possible (even “practically, in real life”) to do all sorts of things that are very difficult to do, like: publish five bestselling novels; climb the highest mountain in every continent; win a gold medal in the Olympics; become a successful movie actor; etc etc.

The point is that questions like yours (which seem to get asked here regularly) are very difficult to give a meaningful answer to. We don’t know you and what you’re capable of. Some people can do the thing you’re asking about, others can’t. So just asking “can it be done” is a question that, if interpreted literally, has an easy answer that’s not very interesting or helpful (“yes, it can be done”), and if re-interpreted to mean what I think you really want to know (“can I , the specific person asking this question, do this thing?”), isn’t a question that I or anyone else here have the information to answer.

Perhaps the most helpful way of addressing the question is to look at the number of people who can do the thing you’re asking about. Well, I’ll be honest, that number is extremely small. Most people who want a career in academia already find it challenging enough to do one PhD in one discipline and then pursue a position as researchers in that one discipline. And even from among those super talented people who find it a bit easier than the rest and are smart and hardworking enough that they can realistically contemplate specializing in two very distant disciplines, one of which being as notoriously difficult as theoretical physics and being acquired purely through self-study, only a vanishingly small fraction of that already small group will care to invest the time and energy that it would take to broaden their reach in such a way. For those people it’s not really about a lack of ability - more about the fact that it’s an inefficient strategy for becoming successful and maximally realizing one’s potential; but it kind of amounts to the same thing in the end.

Summary: can it be done? Yes. Is it something that I’d recommend to anyone to have as their career plan? No.

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    Thank you for this advice! I was looking for a truely practical advice from someone who have a lot of experienced since I was too young to set out a reasonable career plan. – Cuong Nguyen Oct 3 at 6:43
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I'd like to address what I see as a misconception in the original question. You wrote,

I have a fairly good foundation on CS because I have been doing competitive programming since I was 13.

But computer science is not the same as computer programming. Here is an analogy: Computer programming is to computer science as elementary arithmetic is to university-level mathematics. When you were young, you learned how to do addition, multiplication, and so on. As a third-year undergraduate, you've surely been exposed to calculus, linear algebra, differential equations, and so on. No doubt you appreciate that being able to add and multiply is quite different from being able to integrate!

Computer programming is a skill that can be applied in many different fields and in many different ways. It can be applied to AI, but also to mobile phone applications, accounting software, controlling traffic lights, streaming movies, and lots of other things. It's a very practical skill, like being an electrician. Computer science, on the other hand, is more about what we can compute, how fast we can do it, what quality of results we can expect, and so on. It's more like being a physicist with a deep understanding of quantum electrodynamics. That physicist may know how electricity works, but you probably wouldn't ask him to wire your house. In just the same way, there are computer scientists who are terrible programmers.

Given your interests, you might find yourself interested in scientific computing at some point: Writing code to predict the weather, determine the strength of a bridge, simulate electrical circuits, and so on. Or not. It can be hard to know where you'll end up. The wise thing to do is to put yourself in a position where you have the flexibility to pursue your interests.

I don't know you or your situation, so I'm not qualified to make specific recommendations. Instead I'll leave you with a question. Suppose you pursue just one field for the length of your career; somehow you never get the chance to contribute to the other field. Forty years later, which will you regret more: Not researching physics, or not researching AI?

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    Yes, to be clear to the OP, computer science has more to do with pure mathematics and logic than it does computer programming. – Tom Oct 3 at 18:12
  • I think you are the one with the misconception, because competition programming is heavily CS based and not like general software engineering. The comparison of elementary to university mathematics is ridiculous as well because general programming requires a lot of other skills such as writing maintainable code and working in a team. – qwr Oct 3 at 20:34
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    +1 for the question at the end, I think it goes to the core of OP’s dilemma. – Dan Romik Oct 4 at 0:50
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    @qwr There are many different types of programming competitions. We both have preconceptions about their content and value. If the OP chooses to describe his experience in more detail I will update my answer accordingly. Regarding the analogy, it was not meant to be perfect, just illustrative. I still think it was adequate for that purpose. – Ozob Oct 4 at 4:15
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    From my point of view calculus resembles elementary arithmetic much more than it resembles advanced mathematics :). – Denis Nardin Oct 4 at 10:48
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I’d like to offer a complement to Dan Romik’s answer. Roughly summarising, that answer points out that this is possible, but is a challenging plan, and (for most people) very difficult to succeed with — all of which I agree with.

But I wouldn’t therefore advise abandoning the plan entirely. Ambition is great! The important thing with such an ambitious plan is just to aim high, but be aware of the difficulty, and make sure you have a good fallback plan if it doesn’t all succeed. The thing to avoid is just staking everything on an ambitious plan, with no fallback.

For instance, if a PhD student told me they wanted to work on a particularly difficult problem, I would make sure they were aware of how difficult it is, and I would strongly advise them to also work on some more tractable problem at the same time, so they don’t risk ending up with nothing. But I wouldn’t discourage them from pursuing the difficult problem altogether.

In your case, the plan you outline has a built-in fallback: you can, at any point, give up on the physics ambitions and just continue with the PhD in AI. So if that’s a fallback situation you wouldn’t be unhappy with, I’d say go for it on your plan — take the PhD in AI, and self-study in physics on the side as far as your time, energy, and interest allow. (And it may not have to be just self-study: you may be able to take physics courses on the side, if your university allows this.) And be aware that it’s unlikely, though not impossible, that you’ll be able to get into pure theoretical physics research this way; but the physics you learn will almost certainly be useful anyway, as having a knowledge of a wide range topics (and not just the obvious ones) is very valuable for researchers in any field.

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    Hmm. Aiming high is great, but you are ignoring the fact that OP needs to first of all make a decision in which general direction to aim. It’s not enough to have a fallback plan, the fallback plan needs to be something OP can imagine themselves being happy with doing for the rest of their life in the overwhelmingly likely case that it has to be adopted. By giving the advice to “go for it” you are encouraging someone who wants to be a physicist to choose a path that will, with 99.99% chance, lead to them never becoming a physicist. “Go for it”? Sure, but only if you understand the consequences. – Dan Romik Oct 3 at 13:16
  • @DanRomik: Absolutely — I don’t mean to downplay the extent to which the OP should be realistic that the full ambition they outline is unlikely to be attainable. If they are really un-interested in AI in itself, then this wouldn’t be a good plan. But if they’re happy with the idea of most likely ending up as an AI specialist with a strong physics background on the side, then that lets them keep open the ambition of becoming a researcher in both. – PLL Oct 3 at 14:46

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