COMPUTERS AND MORE

Software engineering relies on some of the work from the programming languages community, and deals with the design and implementation of software. Often, software engineering will cover topics like defensive programming, in which the code includes apparently extraneous work to ensure that it is used correctly by others.

Software engineering is generally a practical discipline, with a focus on designing and working on large-scale projects. As a result, appreciating software engineering practices often requires a fair amount of actual work on software projects. It turns out that as programs grow larger, the difficulty of managing them dramatically increases in sometimes unexpected ways.

Systems

Systems work deals, in a nutshell, with building programs that use a lot of resources and profiling that resource usage. Systems work includes building operating systems, databases, and distributed computing, and can be closely related to networking. For instance, some might say that the structure of the internet falls in the category of systems work.

The design, implementation, and profiling of databases is a major part of systems programming, with a focus on building tools that are fast enough to manage large amounts of data while still being stable enough not to lose it. Sometimes work in databases and operating systems intersects in the design of file systems to store data on disk for the operating system. For example, Microsoft has spent years working on a file system based on the relational database model.

Systems work is highly practical and focused on implementation and understanding what kinds of usage a system will be able to handle. As such, systems work can involve trade-offs that require tuning for the common usage scenarios rather than creating systems that are extremely efficient in every possible case.

Some recent work in systems has focused on solving the problems associated with large-scale computation (distributed computing) and making it easier to harness the power of many relatively slow computers to solve problems that are easy to parallelize.

Artificial Intelligence

Last, but not least, is artificial intelligence, which covers a wide range of topics. AI work includes everything from planning and searching for solutions (for instance, solving problems with many constraints) to machine learning. There are areas of AI that focus on building game playing programs for chess and go. Other planning problems are of more practical significance--for instance, designing programs to diagnose and solve problems in spacecraft or medicine.

AI also includes work on neural networks and machine learning, which is designed to solve difficult problems by allowing computers to discover patterns in a large set of input data. Learning can be either supervised, in which case there are training examples that have been classified into different categories (for instance, written numerals classified as being the numbers 1 through 9), or unsupervised, in which case the goal is often to cluster the data into groups that appear to have similar features (suggesting that they all belong to the same category).

AI also includes work in the field of robotics (along with hardware and systems) and multiagent systems, and is focused largely on improving the ability of robotic agents to plan courses of action or strategize about how to interact with other robots or with people. Work in this area has often focused on multiagent negotiation and applying the principles of game theory (for interacting with other robots) or behavioral economics (for interacting with people).

Although AI holds out some hope of creating a truly conscious machine, much of the recent work focuses on solving problems of more obvious importance. Thus, the applications of AI to research, in the form of data mining and pattern recognition, are at present more important than the more philosophical topic of what it means to be conscious. Nevertheless, the ability of computers to learn using complex algorithms provides clues about the tractability of the problems we face.

How to Study Computer Science

If you're an aspiring computer science student or someone who wants to switch fields into CS, you're in luck; there's a lot of information available on the Internet. CS is a large and rapidly-expanding field; once you've become confident in your abilities to program moderate-sized projects, a lot of topics open up to you. But what do you really need to learn about, and what don't you?

A lot of it depends on what you want to get out of your study. If you want to become a researcher, you'll most likely need to know more of the theory of computer science than if you want to become a programmer. There are, however, some basic skills that will help nearly everyone in the field.

Learn Multiple Programming Languages

No matter what you want to do in computer science, you'll likely do some of it by writing computer programs. Not all languages are created equal, but most of htem have some strengths. You'll want to learn a systems language like C or C++. This will give you several advantages: first, you'll understand memory allocation; second, you'll understand more about how the system is designed; and finally, you'll be able to communicate with other programmers more easily. you can see this article for more details on the advantage of learning C .

But you want to learn a more flexible language for daily chores--for instance, a scripting language like Perl or Ruby will help you quickly create interesting programs and test ideas.

Finally, once you've mastered a language or two, expand your horizons with a functional language like Scheme, ML, or Haskell. This will improve your understanding of programming languages and broaden your horizons about the possibilities.

A key thing to remember when learning new languages is that all languages offer the same power--you can do anything in one language that you can in another--but some languages make it easier to do certain things. For instance, if you want to read data from a text file, Perl is a great language. If you want to write an AI engine, then you might be better off with Scheme.

Learn to Design

Whether you want to work as a software engineer or as an academic, you're going to have to design programs in some form or another. Learning good design principles early will make your life easier. The key thing to remember about design is that the goal is to catch the problems before you've committed to a solution that won't let you fix them. You don't have to do all of your design up front, but if you don't, then you'll want to leave more flexibility later.

Of course, some amount of design is absolutely crucial or you'll simply have no idea what should be flexible and what can be hard coded. Overly modular designs can be as deadly and difficult to maintain as extremely inflexible designs. One way of looking at the issue is that modularity is powerful because it makes it easier to replace a bad idea with a good one. But if you know what the good idea is going to be anyway, then modularity doesn't help you, and because it takes more effort, it can hurt you.

A good way to learn design is to practice on well-known systems projects, like writing an interpreter or web server. These kind of projects have the advantage of having well-known implementions that you can look at once you realize that there are problems with your original design. Whatever you do design, you definitely want to implement at least parts of your designs or you'll never really come to understand the drawbacks in your ideas. It's running into these drawbacks that will teach you the most.