Why I am a Computer Scientist

I suppose for those who know me in person are aware of my joy and passion of the study of Computer Science. For those who haven't met me, or don't know much about me here is a brief profile of me:

• I am a Computer Science student at the University of Waterloo
• I am an entrepeneur that is working on several ventures that create software solutions that deal with the manufacturing and the operations related systems

In any event I am a Computer Scientist for one simple reason: the power of computing, be it small scale personal computing or large scale mainframe/server computing has a lot of potential, but unfortunately our industry has only begun to scratch the surface. As an academic who studies in this field it is surprising to believe that current research in Computer Science is the same research that took place some 20 or 30 years ago. Clearly computing has alot of potential but we have made only small progress in this area. We have only begun to scratch the surface of what computing means.

What I find interesting is that we are living in a world which demands increasing amounts of productivity. We must be able to do more with less and be able to make better decisions today than we did yesterday. Unfortunately we don't have the tools available to us to meet these goals in a timely and effective manner for one simple reason. The Computing industry has not caught up to the demands and needs of the marketplace and the real economy. You might be asking yourself what is the real-economy?

The real-economy is a rather new term, at least for myself, and essentially it is the economy that you and I and everyone in the marketplace operates in. It is a term that describes the economic transactions that take place everyday around the world. What I am trying to say is that the marketplace is asking more, but our technology isn't supporting this demand. Therefore we as Computer Scientist must research ways to keep computing relevant to the modern real-economy.

Therefore we must embark on several different strategies. First and foremost we must rethink what software is. Currently the software industry is driven by this notion of, "If you build it, they will come," that is if we build a software solution, because the software solution exists in the first place the marketplace will naturally adapt to the software solution and use it on a day-to-day basis. Microsoft and other companies have shown that this ideology is absolutely incorrect. You need to ask yourself how much software do I have on my computer? How much of the software do I actually use? It turns out that we install alot of promising software but never end up using it because the design of the software prohibits us to effectively extract the usefullnes of the software from it. User interfaces have become increasingly complex not due to increasing functionality but a lack of thoughtfulness on the side of developers. It seems that software that is truly successful (think of iTunes perhaps), is so successful not because of the bells and whistle but because the software is so easy to use that the user's can extract value out of the software instantly. Software should adapt to users rather than users having to adapt to software because most humans are impatient and are unwilling to adapt to a piece of software every time a new piece of software lands on their desk!

This brings me back to my original point. How do we make software that allows individuals and organizations to do more with less? As I eluded to earlier, we software must adapt to users needs so that there is less time and resources spent hunting for answers. This brings me to my second concept:

We must stop hunting for solutions. Today, it seems that to get anything done, we need to hunt for things. We use Google to hunt for fragments of information (even though Google is fundamentally a poor research tool). We hunt around and fiddles with multiple windows and applications to find the fragments of data that we need. The problem with computing is that computers are great at storing data but poor at yielding results. What do I mean by this? Well most people believe that data and information are the same thing, however they are not! Data is a single or series of points which by themselves are meaningless and don't yield truth. Information in the synthesis of truths (data) in a meaningful way to yield results. Therefore, we must design information systems that can take data and using known systems organize it into meangingful and actionable information (something Google can't do!). We shouldn't have to search for the information we need, our computers should present the information to us, sensing that we need the information at that time. We must have the right information at the right time to make the right timely decisions that are needed in today's marketplace.

This is precisely where Computer Science fails at becoming a liberal field. We haven't pushed the envelope as to what precisely we can do with computation. We haven't gotten to the point where we can do what I have described even though it would be of great benefit and would be highly sought after by the marketplace. This is why I am in Computer Science so that I can push the boundaries of the field and begin to really dig into what computers can do. Anyways those are just my thoughts...