Funding Research

The funding of research (and machine learning research) is an issue which seems to have become more significant in the United States over the last decade. The word “research” is applied broadly here to science, mathematics, and engineering.

There are two essential difficulties with funding research:

1. Longshot Paying a researcher is often a big gamble. Most research projects don’t pan out, but a few big payoffs can make it all worthwhile.
2. Information Only Much of research is about finding the right way to think about or do something.

The Longshot difficulty means that there is high variance in payoffs. This can be compensated for by funding many different research projects, reducing variance.

The Information-Only difficulty means that it’s hard to extract a profit directly from many types of research, so companies have difficulty justifying basic research. (Patents are a mechanism for doing this. They are often extraordinarily clumsy or simply not applicable.)

These two difficulties together imply that research is often chronically underfunded compared to what would be optimal for any particular nation. They also imply that funding for research makes more sense for larger nations and makes sense for government (rather than private) investment.

The United States has a history of significant research, and significant benefits from research, but that seems to be under attack.

1. Historically, the old phone monopoly ran Bell Labs which employed thousands doing science and engineering research. It made great sense for them because research was a place to stash money (and evade regulators) that might have some return. They invented the transistor, the laser, and unix. With the breakup of the phone monopoly, it no longer made sense, and so it has been broken apart and has lost orders of magnitude of staff.
2. On a smaller scale, Xerox Parc (inventors of mice, ethernet, and other basic bits of computer technology) has been radically scaled back.
3. IBM and HP, who have been historically strong funders of computer-related research have been forced to shift towards more direct research. (Some great research still happens at these places, but the overall trend seems clear.)
4. The NSF has had funding cut.

What’s Left
The new monopoly on the block is Microsoft, which has been a significant funder of new research, some of which is basic. IBM is still managing to do some basic research. Many companies are funding directed research (with short term expected payoffs). The NSF still has a reasonable budget, even if it is a bit reduced. Many other branches of the government fund directed research of one sort or another. From the perspective of a researcher, this isn’t as good as NSF because it is “money with strings attached”, including specific topics, demos, etc…

Much of the funding available falls into two or three categories: directed into academia, very directed, or both. These have difficulties from a research viewpoint.

1. Into Academia The difficulty with funding directed into academia is that the professors who it is directed at are incredibly busy with nonresearch. Teaching and running a university are full time jobs. It takes an extraordinary individual to manage all of this and get research done. (Amazingly, many people do manage, but the workload can be crushing.) From the perspective of funding research, this is problematic, because the people being funded are forced to devote much time to nonresearch. As an example from machine learning, AT&T inherited the machine learning group from Bell Labs consisting of Leon Bottou, Michael Collins, Sanjoy Dasgupta, Yoave Freund, Michael Kearns, Yann Lecun, David McAllester, Robert Schapire, Satinder Singh, Peter Stone, Rich Sutton, Vladimir Vapnik (and maybe a couple more I’m forgetting). The environment there was almost pure research without other responsibilities. It would be extremely difficult to argue that a similar-sized group drawn randomly from academia has had as significant an impact on machine learning. This group is gone now, scattered to many different locations.
2. Very directed It’s a basic fact of research that it is difficult to devote careful and deep attention to something that does not interest you. Attempting to do so simply means that many researchers aren’t efficient. (I’m not arguing against any direction here. It makes sense for any nation to invest in directions which seem important.)

The Good News (for researchers, anyways)
The good news at the moment is outside of the US. NICTA, in Australia, seems to be a well made attempt to do research right. India is starting to fund research more. China is starting to fund research more. Japan is starting to fund basic research more. With the rise of the EU more funding for research makes sense because the benefit applies to a much larger pool of people. In machine learning, this is being realized with the PASCAL project. On the engineering side, centers like the Mozilla Foundation and OSDL (which are funded by corporate contributions) provide some funding for open source programmers.

We can hope for improvements in the US—there is certainly room for it. For example, the NSF budget is roughly 0.3% of the Federal government budget so the impact of more funding for basic research is relatively trivial in the big picture. However, it’s never easy to tradeoff immediate needs against the silent loss of the future.