What is the target of the research work from your point of view? As for me, the most frequent question I have been asking myself before is that "what is the stuff I am interested in and would that turn out to be some significant contribution?" However, I realize that this is not a target but more a sort of motivation.

There might be two ways to explain the target I guess, one is simply and straight - Can the solution to the problem be generalized and applied to industry?; the other one would be more substantial and complicated, - What is/are the most important problem(s) in the domain? Essentially, the first way needs to well define the "problem" itself as well, which in turn more or less is reflected from those prime ones.In summary, is the target of the research is to try to locate and resolve the most important problems? If it is, what do you think are the most important ones in your domain?

This does seem to be over, since the problems will always change (maybe not as quick as everyday :-)), with a keen eye on the new trend. Thus from now on, it seems all logic works out - there is a defined, 'right' way to try to do good research:

Great research = (
Good background (to be able to locate(distill) or understand new problems)
+ Sensitivity (to the change)
+ Reactivity (the speed to be able to leading not tracing)
+ Distributability (Unfortunately, I realize that good research work does not sell it self.)
)
* Health (The reason to "times" health is that without health, all going to be zero.)
However, this is pretty much like a kitten tracing its own tail, the only difference is that the kitten is growing - the tail is always there, so is the problem. Is there really another way to do the work, another more important and primary way? Or, just the unattainable human desire to try to solve all the possibilities in one shot?

Unsolved problems in Computer Science

At the level of subatomic particles, the universe is stochastic, not deterministic.

The biggest unsolved problem in computer science is how to apply massively parallel computers to solve fundamentally serial problems. There are four physical limits which between them impose a ceiling: the speed of light, the size of an atom, the time it takes for an electron to change state, and Planck's constant. The speed of light limits the rate at which signals can propagate. (In half a nanosecond, light travels fifteen centimeters.) The size of an atom imposes a minimum size on a gate. The electron state change time imposes a minimum time that a transistor can take to change state. Planck's constant is also a hard limit but more esoteric, because it controls "tunneling". It forces you against the other three walls.

(Bill Gates college tour visited Columbia yesterday and the computer science faculty there presented Bill wit h a list of the top five remaining computer science problems.) See Kevin Schofield's blog: http://spaces.msn.com/kevinonthejob/Blog/cns!1ptWhvkELF9sf9D96FilMvRg!127.entry

1. How do we prove that certain problems are hard to solve? This actually related to security and cryptography, because it relies upon using algorithms that are provably expensive to compute.

2. How can we make truly reliable software? Is there something analagous to Shannon's work on error correction and von Neumann's work on reliability through redundancy?

3. How should we architect systems so that they can be more easily maintained and evolved?

4. How can we make computers understandable and usable for people of all backgrounds, ages, and abilities, in the many different situations we encounter in life?

5. How can we program computers to have human qualities such as consciousness, intelligence and emotion?

6. Concurrency. Since CPU clock speeds seem likely to max out at around 5 GHz, Intel and AMD are moving towards multi-core and other multi-processors architectures. But we won't be able to fully utilize that for extra power until we really solve the problems of how to write solid, reliable concurrent progams. Computer scientists have dabbled in this for more than twenty years, but it's never been a priority. That is quickly changing.

Richard Hamming: You and Your Research
Talk at Bellcore, 7 March 1986
One of the characteristics of successful scientists is having courage. Once you get your courage up and believe that you can do important problems, then you can.
people are often most productive when working conditions are bad.
Just hard work is not enough - it must be applied sensibly.
Great scientists tolerate ambiguity very well. They believe the theory enough to go ahead; they doubt it enough to notice the errors and faults so they can step forward and create the new replacement theory. If you believe too much you'll never notice the flaws; if you doubt too much you won't get started. It requires a lovely balance.
Great contributions are rarely done by adding another decimal place. It comes down to an emotional commitment.
"What important problems are you working on in your field?''
If you do not work on an important problem, it's unlikely you'll do important work. It's perfectly obvious. Great scientists have thought through, in a careful way, a number of important problems in their field, and they keep an eye on wondering how to attack them.
You should do your job in such a fashion that others can build on top of it,
It is a poor workman who blames his tools - the good man gets on with the job, given what he's got, and gets the best answer he can.
it is not sufficient to do a job, you have to sell it.
Yes, doing really first-class work, and knowing it, is as good as wine, women and song put together.
the value is in the struggle more than it is in the result.
you cannot be original in one area without having originality in others.
changed the viewpoint and what was a defect became an asset.
they don't work on important problems, they don't become emotionally involved, they don't try and change what is difficult to some other situation which is easily done but is still important, and they keep giving themselves alibis why they don't. They keep saying that it is a matter of luck.
Since from the time of Newton to now, we have come close to doubling knowledge every 17 years, more or less.