Computer Science

Computer Science is a field of study that has something, but necessarily everything, to do with the machines we now call computers.

Definitions

Wikipedia’s Definition, as of early spring, 2026:

Computer science is the study of computation, information, and automation.

Nice and to the point. My definition is a little more expansive:

Computer Science is the theory and practice of computation, knowledge representation, language, intelligence, learning, and consciousness.

A shorter definition, relating it to other broad fields of study:

Philosophy: is concerned with the search for fundamental truths of the universe using logic (reasoning).
Mathematics: is concerned with reasoning on a formal basis (symbol manipulation).
Computer Science: is concerned with automating the reasoning process.

The latter definition makes clear that artificial intelligence, and in particular generative AI, is a subfield of computer science.

Artificial Intelligence is a subfield of Computer Science.

This is probably worth repeating:

Artificial Intelligence is a subfield of Computer Science.

Exercise: How many times have you heard people say “The AI did this” when all they meant by “AI” was just a computer program?

Please read what Wikipedia has to say about Computer Science. This is a great article, and does a great job explaining the field and dispelling common misconceptions.

Questions Computer Scientists Ask

These are the questions that characterize computer science:

What can and can not be computed?
How fast can a certain computation be carried out?
How much information is needed to carry out a certain computation?
How can information be efficiently (and securely) encoded, stored, retrieved, and transmitted?
How do we design programs?
How do we know a program is correct?
How can computational theories help explain intelligence and consciousness?

None of these requires a computer to answer, right?

Wait—there’s one more question: Does P = NP?

What Computer Scientists Do

In case anyone asks you, computer scientists:

Architect large software systems, i.e., identify the components and their behavior and interaction (this requires skill and enormous experience)
Organize information into data structures for efficient processing
Devise algorithms to carry out the specified behavior of the components (this is a creative, artistic process)
Express algorithms (this is called programming)
Validate algorithms (using formal proof and heavy mathematics)
Analyze algorithms (usually to determine whether they satisfy efficiency constraints)
Test code (to increase confidence that a system meets its specification)

Here’s a more poetic answer to what computer scientists do:

Booch computer scientist quote

Subject Areas

The ACM, IEEE-CS and the AAAI have gotten together and defined a set of knowledge areas for computer science.

Knowledge Area	Code	Some Topics
Algorithmic Foundations	AL	Foundational Data Structures and Algorithms • Algorithmic Strategies • Complexity Analysis • Computational Models and Formal Languages • Algorithms and Society
Architecture and Organization	AR	Digital Logic and Digital Systems • Machine-Level Data Representation • Assembly Level Machine Organization • Memory Hierarchy • Interfacing and Communication • Functional Organization • Performance and Energy Efficiency • Heterogeneous Architectures • Quantum Architectures
Artificial Intelligence	AI	Fundamental Issues • Fundamental Knowledge Representation and Reasoning • Machine Learning • Applications and Societal Impact • Probabilistic Representation and Reasoning • Planning • Logical Representation and Reasoning • Agents • Natural Language Processing • Robotics • Perception and Computer Vision
Data Management	DM	The Role of Data • Core Database Systems Concepts • Data Modeling • Relational Databases • Query Construction • Query Processing • DBMS Internals • NoSQL Systems • Data Security & Privacy • Data Analytics • Distributed Databases • Cloud Computing • Semi-structured and Unstructured Databases • Society, Ethics, and Professionalism
Foundations of Programming Languages	FPL	• Compiled vs Interpreted Languages • Scripting • Object-Oriented Programming • Functional Programming • Logic Programming • Event-Driven and Reactive Programming • Parallel and Distributed Computing • Type Systems • Systems Execution and Memory Model • Language Translation and Execution • Program Abstraction and Representation • Syntax Analysis • Compiler Semantic Analysis • Program Analysis and Analyzers • Code Generation • Runtime Behavior and Systems • Advanced Programming Constructs • Language Pragmatics • Formal Semantics • Formal Development Methodologies • Design Principles of Programming Languages • Quantum Computing
Graphics and Interactive Techniques	GIT	Fundamental Concepts • Rendering • Geometric Modeling • Shading • Computer Animation • Visualization • Immersion (MR, AR, VR) • Interaction • Image Processing • Tangible/Physical Computing • Simulation
Human-Computer Interaction	HCI	Understanding the User • Accountability and Responsibility in Design • Accessibility and Inclusive Design • Evaluating the Design • System Design
Mathematical and Statistical Foundations	MSF	Discrete Mathematics • Probability • Statistics • Linear Algebra • Calculus
Networking and Communication	NC	Networked Applications • Reliability Support • Routing And Forwarding • Single-Hop Communication • Mobility Support • Network Security
Operating Systems	OS	Role and Purpose of Operating Systems • Principles of Operating System • Concurrency • Protection and Safety • Scheduling • Process Model • Memory Management • Device Management • File Systems API and Implementation • Virtualization • Real-time and Embedded Systems • Fault Tolerance
Parallel and Distributed Computing	PDC	Parallel Programs • Parallel Communication • Parallel Coordination • Parallel Evaluation • Parallel Algorithms
Security	SEC	Foundational Security • Defensive Programming • Cryptography • Security Analysis and Engineering • Digital Forensics • Security Governance
Society, Ethics, and the Profession	SEP	Social Context • Methods for Ethical Analysis • Professional Ethics • Intellectual Property • Privacy and Civil Liberties • Communication • Sustainability • History • Economies of Computing • Security Policies, Laws and • Computer Crimes • Equity, Diversity and Inclusion
Software Development Fundamentals	SDF	Fundamental Programming Concepts and Practices • Development of Fundamental Data Structures • Development of Algorithms • Software Development Practices
Software Engineering	SE	Teamwork • Tools and Environments • Product Requirements • Software Design • Software Construction • Software Verification and Validation • Refactoring and Code Evolution • Software Reliability • Formal Methods
Specialized Platform Development	SPD	Web Platforms • Mobile Platforms • Robot Platforms • Embedded Platforms • Game Platforms • Interactive Computing Platforms
Systems Fundamentals	SF	Basic Concepts • Resource Allocation and Scheduling • System Performance • Performance Evaluation • System Reliability • System Security • System Design

See, again, AI is just a subfield of Computer Science.

More Subject Areas

Here’s a finer breakdown of subject areas within computer science:

Algorithms
Artificial Intelligence
Compilers and Interpreters
Computation Theory
Computer Architecture
Computational Complexity
Computational Geometry
Data Mining
Data Structures
Databases
Decision Support Systems (Knowledge-Based Systems)
Distributed, Parallel, and Cluster Computing
Digital Libraries
Graphics
Human Computer Interaction
Information Theory
Machine Learning
Modeling in Science, Engineering, and Finance
Multiagent Systems
Multimedia
Natural Language Processing
Networks and Internets
Neural and Evolutionary Computing
Numerical Analysis
Operating Systems
Programming Languages
Real-time Systems
Robotics
Scientific (Numeric, Symbolic) Computation
Search and Information Retrieval
Security (including Cryptography and Cryptanalysis)
Software Engineering
Virtual Reality (and Virtual Worlds)
Vision and Pattern Recognition
Visualization

Exercise: Ask a friend, or a popular chatbot, if there is anything that can be added to this list.

Recurring Concepts

Computer scientists have a deep understanding of the following concepts.

Automation
Binding
Communication
Complexity
Consistency and Completeness
Efficiency
Evolution
Formal Models
Levels of Abstraction
Ordering in Space
Ordering in Time
Reuse
Security
Tradeoffs and Consequences

If these seem abstract, it’s because they are. Computer Science is an academic discipline. There is a real science there. And nearly every computer scientist has become familiar with the following quote, attributed to E. W. Dijkstra:

Computer science is no more about computers than astronomy is about telescopes.

Kinds of Software

People use principles form computer science to create lots of different types of systems. Here’s a rough list of (overlapping) software system categories:

Information Systems that create, store, retrieve, manipulate, present, and destroy information (such as in databases).
Technical Systems such as control equipment in telecommunication, military, or industrial production.
Embedded Real-time Systems which deal with physical sensors and actuators; you see these most often in run in cars, aircraft, appliances, etc.
Distributed Systems which contain many different physical computers connected via some network.
System Software, which is the low level code for operating systems, database systems, and user interfaces.
Application Software like games or word processors, which run on desktops, laptops, and mobile devices.

Speaking of software, it’s useful to point out that software runs on many different kinds of devices, and computer scientists need to take into account how people use these devices when writing software for them. Devices include:

Desktops
Laptops
Phones
Tablets
Robots
Drones
Smart TVs
Game consoles
Machinery
All kinds of crazy things

Related Fields

Computer science borrows from, and touches, a whole lot of other fields, including:

Linguistics (probably the closest relative)
Biology (computational biology, biological databases, etc.)
Psychology (we’d like computers to think like us, and perhaps understand us)
Electrical Engineering (because face it, electronic components make good computing devices!)
Mathematics (especially Discrete Math, Game Theory, etc.)
Education (because both fields are concerned with knowledge representation and learning)
Philosophy (especially Logic, Epistemology, Philosophy of Mind)
Economics (think markets!)
Sociology (computers in society, social networking, Second Life and other virtual environments)
Animation

People

Wikipedia has a list of famous computer scientists. If you want to read about some of these people, don’t skip Alan Turing, Grace Hopper, and Alan Kay.

Two Big Ideas

You’ve undoubtedly noticed, when using your phone, tablet, or laptop, all your programs are running on the same device. You don’t need a separate machine for each of those different things!. You don’t need one machine for adding numbers, one for communicating by voice to other people, one for telling the current time, one for telling you how far away your destination is, one for finding the number of days between two dates, one for maintaining your TO-DO list, one for telling you your current direction, one for timers and alarms, one for translating Irish poems into Swahili; you get the idea.

You just need one machine.

But this was not always obvious. It was not obvious until the 1930s or later. People had to ask the deep question “What even is computation?” and get it onto a formal footing, for the field of Computer Science to arise. Alan Turing answered that question by representing computation as a “machine” described as a mathematical object. Then he showed every machine could be simulated by a single machine (since named the Universal Turing Machine in his honor).

That was stunning. Brilliant. The first big idea is universality.

Exercise: What does that supercomputer in your pocket do that separate “single-function” devices used to (or still) do? Come up with a list of 20 things. Here are some things to get you started: set and activate alarms, make phone calls, tell the time, count your steps, navigate, manage your appointments). What things do they not currently do that you think they should do?

But there’s one more big idea: Turing was able to show (others discoverd this independently by the way) that there were some problems that could not be solved (a.k.a. functions that could not be computed, a.k.a. questions that could not be answered) by any machine. The second big idea, then, is called undecidability.

Here is one such problem.

Exercise: Memorize the two big ideas and be able to explain them. It’s good to have these explanations handy in case people every ask you “Oh you are a computer scientist? What is that all about? What do you do?”

Summary

We’ve covered:

Definitions of Computer Science
AI is a subfield of computer science
What computer scientists are concerned with and what they do
Subject areas and recurring concepts
Which fields are related to computer science
Two big ideas in CS: (1) universal computing machines exist, and (2) not everything is computable