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 research very risky (especially if the researcher has a tendency to look for elegant mathematical solutions), but literally no one will pay you for it. He insisted that you won't find any companies doing long term basic research because it won't benefit them more than competitors in the long run.
One counter example to this might be Willow Garage. However, I think they still are not doing the very basic theoretical math research that they will actually wish they had once a personal robotics industry does start booming. I've really racked my brain trying to come up with places that actually pursue the theory because of the long term practical benefits.
Moreover, I am very discouraged about the state of academic publishing right now. The main reason I want to hack myself is to change my preferences about being a university researcher. Currently, I see only two alternatives: university researcher or corporate/industrial/government researcher. I had always thought that in the former, people paid you grant money because of your ingenuity and foresight and the whole point was to use tax money to allow researchers to conduct high-risk research that commercial entities could not afford to risk their money on. As it turns out though, both of these options require you to pander to whatever the popular commercial interests of your day happen to be, even if you think that the popular commercial interests have got it all wrong and are going down the wrong track.
It makes me feel that I need to hack myself to want to want to just be a programmer for some company somewhere. Make enough money to do cryonics and have an internet connection and just float along. I feel very discouraged to really try anything else. And since my current preferences hold that computer programming for the sake of widget building is soul-crushingly terrible, I feel like it's a big Catch-22 putting me in an ambivalent stalemate.
Lastly, the altruism argument you mention doesn't appeal to me. I think society should have a tenure class of professors able (and required) to do riskier / theoretical research. The amount of work it takes to get to that position in life ought to outweigh whatever solely-free-market altruism a corporate scientist might be prideful of. But the reality is that by the time I am in a position to seriously apply for a tenure track job (2 more years of school, 2 years of post-doc, 5 years as assistant professors, so roughly 9 years from now), tenured positions just simply will not exist. It's already a dying business model, which makes me feel like all of the time I've already spent training non-practical skills into myself was a massive unforeseen waste.
It wasn't that I know of an existing organization that does what you want. It's more that there exist things out there (like the SENS foundation, or Halcyon Labs, or SIAI itself) designed to do science in other fields. I agree that it would be hard to move into the "computer vision start-up" space with a more long-term focus, at least these days.
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?"