5 ms·

What's the difference between a "problem" and a "research area"? Research areas are made of problems to solve.

dbmikus 3y ago
Agree, distinction is fuzzy. I meant, instead of picking a research area because it seems cool/interesting/etc, either: 1. pick a product or user problem and try to solve that problem, then work back into the research you need to do so 2. pick a research problem and try to solve that (again, focusing on the problem, not just an area of study) The blog post seemed to be more for people doing applied things, so I was speaking to those doing option 1.
- mathisfun123 3y ago
  >focusing on the problem, not just an area of study how exactly do you plan on picking a problem that is either unsolved and/or not uninteresting without "picking a research area"? solving an already solved problem is a waste of your time (reviewer #2 will just point out the relevant missing citation). solving an uninteresting problem is a waste of everyone's time: it will take someone some time to figure out what you've actually done and that it's useless. this is why generic platitudes like this are worse than useless - you're giving the impression/appearance of high wisdom that's sure to lead some naive kid astray; for example, the bulk of a phd is not actually solving some problem but finding the right problem to solve (interesting and unsolved).
  - dbmikus 3y ago
    I should probably have not mentioned the academic use case. My advice is better for people trying to solve real-world problems, as opposed to improving the theory. Not to say that improving the theory doesn't later lead to massive real-world solutions! I feel very strongly, that if someone wants to apply ML to accomplish something outside of academia, they should think about an applied use case and then work backwards to what they need to learn. Otherwise, you will have a solution in search of a problem. If someone wants to go into academia or theory, then yes, I think you're right. They need to pick a research area. But then I think the goal should be get to the problem space as soon as possible. I think it would be suboptimal to decide "I want to improve Bayesian ML" without first deciding the "why", such as: "I want to make ML models more understandable." And yeah, maybe you need to do a little research to know what the problems-to-solve are first. Per all the above, I never went into academia, so take my opinion there with a grain of salt. I have worked exclusively at startups and co-founded one, so take my opinion there with two grains of salt :)

Agree, distinction is fuzzy. I meant, instead of picking a research area because it seems cool/interesting/etc, either: 1. pick a product or user problem and try to solve that problem, then work back into the research you need to do so 2. pick a research problem and try to solve that (again, focusing on the problem, not just an area of study) The blog post seemed to be more for people doing applied things, so I was speaking to those doing option 1.

mathisfun123 3y ago
>focusing on the problem, not just an area of study how exactly do you plan on picking a problem that is either unsolved and/or not uninteresting without "picking a research area"? solving an already solved problem is a waste of your time (reviewer #2 will just point out the relevant missing citation). solving an uninteresting problem is a waste of everyone's time: it will take someone some time to figure out what you've actually done and that it's useless. this is why generic platitudes like this are worse than useless - you're giving the impression/appearance of high wisdom that's sure to lead some naive kid astray; for example, the bulk of a phd is not actually solving some problem but finding the right problem to solve (interesting and unsolved).
- dbmikus 3y ago
  I should probably have not mentioned the academic use case. My advice is better for people trying to solve real-world problems, as opposed to improving the theory. Not to say that improving the theory doesn't later lead to massive real-world solutions! I feel very strongly, that if someone wants to apply ML to accomplish something outside of academia, they should think about an applied use case and then work backwards to what they need to learn. Otherwise, you will have a solution in search of a problem. If someone wants to go into academia or theory, then yes, I think you're right. They need to pick a research area. But then I think the goal should be get to the problem space as soon as possible. I think it would be suboptimal to decide "I want to improve Bayesian ML" without first deciding the "why", such as: "I want to make ML models more understandable." And yeah, maybe you need to do a little research to know what the problems-to-solve are first. Per all the above, I never went into academia, so take my opinion there with a grain of salt. I have worked exclusively at startups and co-founded one, so take my opinion there with two grains of salt :)