I'm no expert in the fields, but it appears that this is to a smaller extent true in some fields of health, where it relates to nutrition/lifestyle(particularly stress)/epigenetics. They're starting to be asked, but the idea that your genetic expression changes (and that changed expression can be passed on, possibly skipping generations), it fairly new too.
oh, hah. reading comprehension fail. an undergrad education is definitely *not* enough to answer the question here. unfortunately.
stem cell research,
many subfields of cognitive science
Many of these you could ask intelligent questions with just a BA, but the equipment needed to answer them is more specialized and expensive.
also, behavioral economics, which is basically psychology discovered through the use of statistics and large experiments.
and while I'm on this roll, let me add physics. Because graduate string theory really doesn't teach you anything.
er, theoretical unified everything physics...yeah, okay, that wasn't a typo, just a casualo.
Sounds about right. I might even add straight-up neuroscience. We know so little about how the brain does what it does.
yeah. The dividing line between we know a lot and we know little is, entertainingly, the same dividing line as in computer science -- we know a fair bit about the individual neuron, but not much about networks of them. Which is, of course, a computation problem, though analog and involving delicate physical apparati.
2009-02-17 02:05 am (UTC)
I disagree. Neuroscience needs some complicated and expensive equipment to perform its experiments. Unless you are positing some sort of theoretical neuroscience, which may as well be described as "guessing".
We're all confusing two separate issues, and I don't know which is of importance to you.
* the ability to ask useful questions; to design useful experiments; the experience to do the above well
* the equipment to do those experiments
Computer science is special in that the equipment is cheap and readily available. The other part, lots of fields have. Most of those other fields either require expensive equipment or large numbers of
I'm not sure I believe that CS research is cheap, not when it comes to the specific subfields Peter mentioned. If one wants to explore new questions in networks and UI, I would think one would need to build networks and UIs that nobody has built before. Actually, to get there, one would probably need to build, throw away, build, throw away...it's starting to sound expensive.
I'm studying programming languages. The equipment is definitely cheap and readily available. But it's one of the older areas of CS. I don't think a BS is enough to start answering the questions in PL.
Then again, what do I know? I don't have a BS.
Edited at 2009-02-18 03:13 am (UTC)
Networks and UI research are still cheap when compared to anything else we've talked about on this post. He's making the assumption that your labor is free, which is a reasonable assumption in the case of a research interest. However, if it weren't, it would raise all the fields equally -- no new question is easy to answer, all require significant labor.
And setting up a dozen computers costs very little compared to building neuroimaging.
And setting up a dozen computers costs very little
But we're talking about answering new questions. Aren't a lot of those questions going to be questions of scale?
Yeah, I think so. One of the reasons computer science keeps being so interesting is that we keep having to deal with problems that we haven't encountered before in terms of scale.
I saw Peter Norvig talk a few weeks ago when I was in Mountain View, and the gist of his talk was "More data, more better." But how are we going to deal with all that data? Every day we have more than we had yesterday! Better figure it out.
Indeed, one reason I'm so excited about programming that treats computation as the evaluation of mathematical functions is that it can be a natural way to approach solving huge-scale problems which want to be broken up into lots of pieces to be solved independently and in parallel.
2009-02-18 02:42 pm (UTC)
Although it should be pointed out that Peter Norvig is at the forefront of a group of AI researchers who think that intelligence is based on lots and lots of data with relatively simple algorithms.
I think in CS we have more problems than scale, although in networks there' a lot of scaling-type-stuff. But most people build and use simulators rather than building out any hardware, and simulators can run on just about any hardware.
"There are only two hard problems in Computer Science: cache invalidation and naming things. Phil Karlton"
This is manifestly NOT true in math
One thing I’ve learned during this job search is that many math departments want new faculty members who can propose and direct undergraduate research projects, and have some idea how they might involve undergraduates in their research program. I’ve been asked about this in pretty much every interview I’ve had so far.
2009-02-17 09:49 pm (UTC)
Right, but you have to VERY carefully architect your research around that goal in a way you don't have to do in CS (IMO).
Oh, yeah. I can't imagine coming up with good research projects for undergraduates in mathematics is at all easy.
This is manifestly NOT true in math
I'm not sure I agree. One great strength of mathematics, perhaps the greatest, is that it can be very, very, very
specific. By building the right vocabulary and choosing the proper abstractions, you can make it tractable to consider a question that is extremely specific. This increases the chance that it's never been asked before, without necessarly increasing the chance that you need more than a B.S. to tackle it.
Will the question and answer be interesting to more people than just you? I think that depends largely on the simplicity of the construction of the question, and the "beauty factor" of the answer.
However, simple, interesting questions tend to be things that people have already thought of. Generally speaking, the more specific your machinery, the smaller the likelihood of it being (A) applicable to a problem of general interest or (B) easy to use. The rare tools that make good research tend to fulfill (A) but not (B).
Right, undergrads *can* do original research in math, by asking (relatively shallow) questions that have never been asked. (by shallow, I just mean not super deep)
Combinatorics is very fertile ground for coming up with new definitions, which makes it easy to ask original (but useless) mathematically interesting questions. However, for the same reason, it is hard to be sure that your questions haven't been investigated already, using different names for the concepts.
Biochemistry, genetics, and medicine come to mind. In theory, anything that relies on recent technological advances would go in this category, because we haven't been able to study it for long.