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Fields of Computer Science

Computer science is often said to be neither a science nor about computers. There is certainly some truth to this claim--computers are merely the device upon which the complex and beautiful ideas in computer science are tested and implemented. And it is hardly a science of discovery, as might be physics or biology, so much as it is a discipline of mathematics or engineering. But this all depends on which branch of computer science you are involved in, and there are many: theory, hardware, networking, graphics, programming languages, software engineering, systems, and of course, AI.


Computer science theory is often highly mathematical, concerning itself with questions about the limits of computation. Some of the major results in CS theory include what can be computed and how fast certain problems can be solved. Some things are simply imposible to figure out! Other things are merely difficult, meaning they take a long time. The long-standing question of whether "P=NP" lies in the realm of theory.

A subsection of theory is algorithm development. For instance, theorists might work to develop better algorithms for graph coloring, and theorists have been involved in improving algorithms used by the human genome project to produce faster algorithms for predicting DNA similarity.

Cryptography is another booming area of the theory section of computer science, with applications from e-commerce to privacy and data security. This work usually involves higher-level mathematics, including number theory. Even given all of the work in the field, algorithms such as RSA encryption have yet to be proven totally secure.

Work in theory even includes some aspects of machine learning, including developing new and better learning algorithms and coming up with bounds on what can be learned and under what conditions.


Computer hardware deals with building circuits and chips. Hardware design lies in the realm of engineering, and covers topics such as chip architecture, but also more general electrical engineering-style circuit design.


Networking covers topics dealing with device interconnection, and is closely related to systems. Network design deals with anything from laying out a home network to figuring out the best way to link together military installations.

Networking also covers a variety of practical topics such as resource sharing and creating better protocols for transmitting data in order to guarantee delivery times or reduce network traffic.

Other work in networking includes algorithms for peer-to-peer networks to allow resource detection, scalable searching of data, and load balancing to prevent network nodes from exploiting or damaging the network.

Networking often relies on results from theory for encryption and routing algorithms and from systems for building efficient, low-power network nodes.


The field of graphics has become well-known for work in making amazing animated movies, but it also covers topics such as data visualization, which make it easier to understand and analyse complex data. You may be most familiar with the work in computer graphics because of the incredible strides that have been made in creating graphical 3D worlds!

Programming Languages

Programming languages are the heart of much work in computer science; most non-theory areas are dependent on good programming languages to get the job done. Programming language works focuses on several topics. One area of work is optimization--it's often said that it's better to let the compiler figure out how to speed up your program instead of hand-coding assembly. And these days, that's probably true because compiler optimizations can do amazing things.

Proving program correctness is another aspect of programming language study, which has led to a class of "functional" programming languages. Much recent work has focused on optimizing functional languages, which turn out to be easier to analyze mathematically and prove correct, and also sometimes more elegant for expressing complex ideas in a compact way.

Other work in programming languages deals with programmer productivity, such as designing new language paradigms or simply better implementations of current programming paradigms (for instance, one could see Java as an example of a cleaner object-oriented implementation than C++) or simply adding new features, such as garbage collection or the ability to create new functions dynamically, to languages and studying how this improves the programmer's productivity.

Recently, language-based security has become more interesting, as questions of how to make "safer" languages that make it easier to write secure code.

Software Engineering

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 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.
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