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A PhD is a license to do deep research. Deep research careers are very rare, but for the right kind of person, they're great. However, deep research by defini

A PhD is a license to do deep research. Deep research careers are very rare, but for the right kind of person, they're great. However, deep research by definition means it probably won't work, and there's just a lot more money in doing things that will probably work vs things that probably won't. So it helps to be extremely talented at deep research if you want to pursue the PhD. Also, most engineering PhD's are bogus because most engineering "research" is actually not deep research - it's building prototypes that aren't quite useful but not quite that novel or interesting either. If you're in a PhD program and you're not doing something really interesting and fundamental, you're definitely in a tough spot.

tha12 8y ago
What do you consider to be deep research? I agree that engineering research is not actually deep, but once you eliminate that almost all of the current (applied, and maybe theoretical too) Machine Learning work for instance is also dismissed.
- tomrod 8y ago
  > What do you consider to be deep research? As a guiding principle, I think deep research is answering questions that no one has approached in quite the same way before. In my own PhD work, I used the time to learn how to answer questions sufficiently (methodology) as well as how to recognize shape/distill questions in a way that they can be approached. Developing these skills don't require a PhD, but the dedicated time helped me. I've used these skills to learn deeply and answer questions to great extent in business roles and drive some solid change in a few large orgs. It doesn't mean I do a job better than someone without a PhD. Also, the PhD signals that I have answered some deep questions to the satisfaction of others that answer deep questions (PhD committee).
- jingleheimer 8y ago
  That's why it's called Machine Learning and not Artificial Intelligence. It was an intentional differentiation to avoid the pure research academics who start every presentation with 'assuming infinite compute resources'. ML is an Applied Research discipline and all the better for it.
  - nl 8y ago
    There's plenty of academic research in machine learning. I've never been to any academic presentation where they start like that. In-fact, more often they complain about the huge compute resources in industry.
- nl 8y ago
  I don't have a PhD, but I have run research programs. The deeper into a field you get the more you realize that the parts which seem deep research aren't, and the parts which seem incremental improvements are actually very deep. I can think of a number of things in machine learning which appear hard which are easy, and vice versa. Theoretical justification for GAN improvements (eg, the WGAN paper): elegant but obvious, even though I'm not a mathematician. Generative models for text including entities that remain coherent for longer than a sentence? We barely know how to even start thinking about this problem.
  - nu-world-odor 8y ago
    oh yes we do
- btrettel 8y ago
  I'm not who you asked, but here's my current take: Deep research maximizes uncertainty reduction (or information gain in other words). Uncertainty here could be model uncertainty if you are developing models, or a shift in the probability distribution for a particular question more generally. E.g., "Does P=NP?" would be an example of the latter. It might be very general, applicable in many fields. Or it could be targeted at a particular field, but in a way which answers many questions. Bayesian experimental design can do what I think is the easy part of the problem: maximizing information gained for a particular experimental problem statement. In my view, most of the time you can reasonably guess what Bayesian experimental design might tell you by looking at a state space of your exprimental data. So the math may not be strictly necessary. Unfortunately, not all research is experimental. And it won't tell you, for example, if you are missing a variable. Framing the problem (which questions to ask, and how to answer them) seems like the most important part to me. Or at least it has been in my almost complete PhD. These thoughts are in flux. I may have a different view in a year.
wcrichton 8y ago
The phrase "most engineering PhD's are bogus" is astoundingly ignorant of the excellent work that goes on in the systems community (assuming by "engineering" you're referring to application-oriented CS research?). "Building prototypes that aren't quite useful but not quite that novel" is bad systems research, not all systems research.
- nicodjimenez 8y ago
  I agree with you, but I do think it's incredibly hard and almost unnatural to do great engineering research, and that's why it's so rare. It's straightforward to solve someone's problem, it's also straightforward to study a phenomenon in nature (or computers) and learn something about it. It requires a very special type of person to create a new way of thinking that sets the groundwork for building systems that solve people's problems.
deleted 8y ago
[deleted]
kabdib 8y ago
Microsoft encourages its research people to help product teams ship things based on their research. I had two interactions with PhD types on two different products. The first was a researcher who talked to us a few times (just a few hours of meetings) then returned a few months later with a pile of MatLab code and a "problem solved!" attitude -- his simulations showed that things were working great. We looked at the code and while it taught us some things, it was obviously not shippable. None of his stuff wound up in the product, though it did point us down some interesting paths (some good, some bad). Another researcher got a desk smack in the middle of the product team and spent 18 months sitting with us full-time, porting and dramatically improving his algorithms. I'd say that he learned just as much from us as we learned from him. His first few months were rocky, but he eventually became a productive and supportive member of the team. I hope that MSR treated him well upon his return. Personally I think that research is great, but it's fantastic if you can occasionally ship your work to real customers.
LolWolf 8y ago
> Also, most engineering PhD's are bogus because most engineering "research" is actually not deep research. Hello! Engineering researcher here. I toy with things that are both wildly theoretical (information bounds for algorithms, inference in stochastic dynamical systems, etc.) to things that are fantastically and directly useful (design of photonic structures for LIDAR, (much) better AR/VR lenses, etc.). While you're possibly right that I might be "building prototypes that aren't quite useful but not quite that novel," I disagree that they're "[not] interesting." In fact, I'd be absolutely surprised if in a few years, much of the applied "engineering" work our lab does (in contrast to the theoretical work) is not in constant use for on-chip photonics and fabrication of optical structures.
- nicodjimenez 8y ago
  Honestly that sounds like an amazing thing to do a PhD in.

tha12 8y ago

What do you consider to be deep research? I agree that engineering research is not actually deep, but once you eliminate that almost all of the current (applied, and maybe theoretical too) Machine Learning work for instance is also dismissed.

tomrod 8y ago
> What do you consider to be deep research? As a guiding principle, I think deep research is answering questions that no one has approached in quite the same way before. In my own PhD work, I used the time to learn how to answer questions sufficiently (methodology) as well as how to recognize shape/distill questions in a way that they can be approached. Developing these skills don't require a PhD, but the dedicated time helped me. I've used these skills to learn deeply and answer questions to great extent in business roles and drive some solid change in a few large orgs. It doesn't mean I do a job better than someone without a PhD. Also, the PhD signals that I have answered some deep questions to the satisfaction of others that answer deep questions (PhD committee).
jingleheimer 8y ago
That's why it's called Machine Learning and not Artificial Intelligence. It was an intentional differentiation to avoid the pure research academics who start every presentation with 'assuming infinite compute resources'. ML is an Applied Research discipline and all the better for it.
- nl 8y ago
  There's plenty of academic research in machine learning. I've never been to any academic presentation where they start like that. In-fact, more often they complain about the huge compute resources in industry.
nl 8y ago
I don't have a PhD, but I have run research programs. The deeper into a field you get the more you realize that the parts which seem deep research aren't, and the parts which seem incremental improvements are actually very deep. I can think of a number of things in machine learning which appear hard which are easy, and vice versa. Theoretical justification for GAN improvements (eg, the WGAN paper): elegant but obvious, even though I'm not a mathematician. Generative models for text including entities that remain coherent for longer than a sentence? We barely know how to even start thinking about this problem.
- nu-world-odor 8y ago
  oh yes we do
btrettel 8y ago
I'm not who you asked, but here's my current take: Deep research maximizes uncertainty reduction (or information gain in other words). Uncertainty here could be model uncertainty if you are developing models, or a shift in the probability distribution for a particular question more generally. E.g., "Does P=NP?" would be an example of the latter. It might be very general, applicable in many fields. Or it could be targeted at a particular field, but in a way which answers many questions. Bayesian experimental design can do what I think is the easy part of the problem: maximizing information gained for a particular experimental problem statement. In my view, most of the time you can reasonably guess what Bayesian experimental design might tell you by looking at a state space of your exprimental data. So the math may not be strictly necessary. Unfortunately, not all research is experimental. And it won't tell you, for example, if you are missing a variable. Framing the problem (which questions to ask, and how to answer them) seems like the most important part to me. Or at least it has been in my almost complete PhD. These thoughts are in flux. I may have a different view in a year.

wcrichton 8y ago

The phrase "most engineering PhD's are bogus" is astoundingly ignorant of the excellent work that goes on in the systems community (assuming by "engineering" you're referring to application-oriented CS research?). "Building prototypes that aren't quite useful but not quite that novel" is bad systems research, not all systems research.

nicodjimenez 8y ago
I agree with you, but I do think it's incredibly hard and almost unnatural to do great engineering research, and that's why it's so rare. It's straightforward to solve someone's problem, it's also straightforward to study a phenomenon in nature (or computers) and learn something about it. It requires a very special type of person to create a new way of thinking that sets the groundwork for building systems that solve people's problems.

deleted 8y ago

[deleted]

kabdib 8y ago

Microsoft encourages its research people to help product teams ship things based on their research. I had two interactions with PhD types on two different products. The first was a researcher who talked to us a few times (just a few hours of meetings) then returned a few months later with a pile of MatLab code and a "problem solved!" attitude -- his simulations showed that things were working great. We looked at the code and while it taught us some things, it was obviously not shippable. None of his stuff wound up in the product, though it did point us down some interesting paths (some good, some bad). Another researcher got a desk smack in the middle of the product team and spent 18 months sitting with us full-time, porting and dramatically improving his algorithms. I'd say that he learned just as much from us as we learned from him. His first few months were rocky, but he eventually became a productive and supportive member of the team. I hope that MSR treated him well upon his return. Personally I think that research is great, but it's fantastic if you can occasionally ship your work to real customers.

LolWolf 8y ago

> Also, most engineering PhD's are bogus because most engineering "research" is actually not deep research. Hello! Engineering researcher here. I toy with things that are both wildly theoretical (information bounds for algorithms, inference in stochastic dynamical systems, etc.) to things that are fantastically and directly useful (design of photonic structures for LIDAR, (much) better AR/VR lenses, etc.). While you're possibly right that I might be "building prototypes that aren't quite useful but not quite that novel," I disagree that they're "[not] interesting." In fact, I'd be absolutely surprised if in a few years, much of the applied "engineering" work our lab does (in contrast to the theoretical work) is not in constant use for on-chip photonics and fabrication of optical structures.

nicodjimenez 8y ago
Honestly that sounds like an amazing thing to do a PhD in.