I am in very close to the same position as you (applied math grad student with almost the same interests) and I am quite sanguine about the future, barring worries about my own risk of failure.
Mainly because I may be less far along in my research career and I don't yet feel precommitted to any research methods that look like they're not working. Also because I have no real aversion to crass commercialism.
Thought 1: as far as I know, they still use a lot of PDE's in computer graphics. Nobody's going to write an SVM that can replace Pixar.
Thought 2: I don't really believe pure dumb ML can solve the serious vision problems in the long run. It just looks like it works for now because you can throw a lot of processing power at a question. But this is not how your brain does it; there's built-in structure and actual geometric information based on the assumption that we live in a physical world where images come from light illuminating objects. I have heard a few professors lament the shortsightedness of so-called machine vision researchers. If you want to do the deep stuff, maybe the best thing to do is work with one of the contrarian professors. That's (approximately) what I'm doing, though I'm not working on vision at the moment. Or, more speculatively --- there is a trend for some Silicon Valley types to invest in long-term basic research that universities don't support. Maybe you could see if something like that could work for you.
Thought 3: if you're interested in hacking yourself to be okay with not working in academia, consider that it's more altruistic. A professor benefits from taxpayer dollars and the security of tenure (which protects him from competition by newcomers.) A developer in the private sector produces value for the rest of society, without accepting any non-free-market perks.
there is a trend for some Silicon Valley types to invest in long-term basic research that universities don't support.
Can you point me to any specific examples of this? I have a grad student colleague here who is very involved with face detection and tracking and his work has essentially blown the state-of-the-art performance out of the water. Because of this, he's heavily involved with various startups and web businesses looking to use his better face detection methods. When I queried him for advice, he basically said that not only is long-term, basic r...
I was inspired by the recent post discussing self-hacking for the purpose of changing a relationship perspective to achieve a goal. Despite my feeling inspired, though, I also felt like life hacking was not something I could ever want to do even if I perceived benefits to doing it. It seems to me that the place where I would need to begin is hacking myself in order to cause myself to want to be hacked. But then I started contemplating whether this is a plausible thing to do.
In my own case, there are two concrete examples in mind. I am a graduate student working on applied math and probability theory in the field of machine vision. I was one of those bright-eyes, bushy-tailed dolts as an undergrad who just sort of floated to grad school believing that as long as I worked sufficiently hard, it was a logical conclusion that I would get a tenure-track faculty position at a desirable university. Even though I am a fellowship award winner and I am working with a well-known researcher at an Ivy League school, my experience in grad school (along with some noted articles) has forced me to re-examine a lot of my priorities. Tenure-track positions are just too difficult to achieve and achieving them is based on networking, politics, and whether the popularity of your research happens to have a peak at the same time that your productivity in that area also has a peak.
But the alternatives that I see are: join the consulting/business/startup world, become a programmer/analyst for a large software/IT/computer company, work for a government research lab. I worked for two years at MIT's Lincoln Laboratory as a radar analyst and signal processing algorithm developer prior to grad school. The main reason I left that job was because I (foolishly) thought that graduate school was where someone goes to specifically learn the higher-level knowledge and skills to do theoretical work that transcends the software development / data processing work that is so common. I'm more interested in creating tools that go into the toolbox of an engineer than with actually using those tools to create something that people want to pay for.
I have been deeply thinking about these issues for more than two years now, almost every day. I read everything that I can and I try to be as blunt and to-the-point about it as I can be. Future career prospects seem bleak to me. Everyone is getting crushed by data right now. I was just talking with my adviser recently about how so much of the mathematical framework for studying vision over the last 30 years is just being flushed down the tubes because of the massive amount of data processing and large scale machine learning we can now tractably perform. If you want to build a cup-detector for example, you can do lots of fancy modeling, stochastic texture mapping, active contour models, fancy differential geometry, occlusion modeling, etc. Or.. you can just train an SVM on 50,000,000 weakly labeled images of cups you find on the internet. And that SVM will utterly crush the performance of the expert system based on 30 years of research from amazing mathematicians. And this crushing effect only stands to get much much worse and at an increasing pace.
In light of this, it seems to me that I should be learning as much as I can about large-scale data processing, GPU computing, advanced parallel architectures, and the gross details of implementing bleeding edge machine learning. But, currently, this is exactly the sort of thing I hate and went to graduate school to avoid. I wanted to study Total Variation minimization, or PDE-driven diffusion models in image processing, etc. And these are things that are completely crushed by large data processing.
So anyway, long story short: suppose that I really like "math theory and teaching at a respected research university" but I see the coming data steamroller and believe that this preference will cause me to feel unhappy in the future when many other preferences I have (and some I don't yet know about) are effected negatively by pursuit of a phantom tenure-track position. But suppose also that another preference I have is that I really hate "writing computer code to build widgets for customers" which can include large scale data analyses, and thus I feel an aversion to even trying to *want* to hack myself and orient myself to a more practical career goal.
How does one hack one's self to change one's preferences when the preference in question is "I don't want to hack myself?"