Computer science is the holistic study of all aspects of computers, offering a generalist approach to topics ranging from algorithms and programming languages, to artificial intelligence (abbreviated as "AI") and machine learning. In short, artificial intelligence is a sub-discipline of computer science, and machine learning is a sub-discipline of artificial intelligence.
Once the realm of science fiction, decades of technological breakthroughs -- including OpenAI's release of ChatGPT-4 -- are bringing us closer to realizing artificial intelligence. Today, AI is often defined in two ways: first, as the scientific process of designing machines that are capable of human-like, inductive decision-making; and secondly, as the outcome, or the intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals.
The goals and functions of AI are similar to those of machine learning ("ML"), but they use a much wider range of techniques like deep learning, neural networks, robotics, and more. Machine learning is the science of designing autonomous ("self-thinking") software that can "learn" knowledge from big data to solve problems and make predictions at massive scale.
Educational requirements and employment pathways vary for careers across computer science, artificial intelligence and machine learning. That’s why it’s important to identify your personal career goals and interests before pursuing an advanced degree program.
What is Artificial Intelligence?
Artificial intelligence is a sub-discipline of computer science. AI, as a whole, tries to enable computers to mimic human intelligence in order to solve complex problems and make decisions at scale, in a replicable manner. By designing specialized algorithms, computer scientists help machines teach themselves, simulating human cognitive functions. AI-equipped machines are able to gather and process big data from various sources, such as sensors or remote inputs, rapidly analyze the data, and then act on the insight gathered from that analysis.
While it may be a decade or two before programming fundamentally changes, in the future software engineers will become designers, guiding AI as it writes code. Currently, there’s a tremendous shortage of programmers. This technology will help fill that gap and increase production.
- Read more about Rice CS's breakthrough machine learning research.
The AI field draws upon computer science, mathematics, psychology, linguistics, philosophy and many others. Applications of artificial intelligence are innumerable, including healthcare and biomedicine, bioinformatics, physics, robotics, geosciences and more.
What is Machine Learning?
With AI/ML-related terms on the rise in job postings, many prospective students ask, how is machine learning similar to computer science, and is computer science the same as machine learning?
Machine learning is one sub-discipline of artificial intelligence, which seeks to "learn" knowledge from big data to solve complex problems in a replicable, scalable manner. Machine learning experts develop "next gen" algorithms -- machine learning-based algorithms -- that enable computer systems to automate the basic algorithms computer scientists and software engineers design and implement today, without the need for as much manual human intervention. Within companies, data scientists and other R&D/innovation teams then use these ML tools as one way to uncover actionable insights from massive amounts of data they have about their business, customers and competitive landscape.
Machine learning models promise greater precision, accuracy and efficiency, reducing much of the need for developers to manually program instructions for the machines. They're used daily to enhance decision-making in business, healthcare, law enforcement and finance. For example, machine learning algorithms are used for facial recognition, spam filters, and personalized search engine results.
What is Deep Learning?
Deep learning refers to the multi-stage machine learning methods in which machines learn representations of complex data in phases, over time. Deep learning is what it sounds like: a type of machine learning designed to delve much deeper than other forms of ML. Traditional machine learning algorithms are linear, whereas deep learning algorithms are stacked in a hierarchy of increasing complexity.
Over the past several years, thanks for the development of new machine learning (ML) training rules, massive computing capabilities, and enormous training datasets, deep learning systems have redefined the state-of-the-art in object identification, face recognition, and speech recognition. Examples of modern tools include ChatGPT, Deep Mind’s AlphaGo, Facebook’s Deep Face, and Baidu’s Deep Speech.
What is the Difference Between Artificial Intelligence and Machine Learning?
At a macro level, artificial intelligence (AI) is interested in enabling computers to mimic human intelligence in order to solve complex problems and make decisions at scale, in a replicable manner. Machine learning is one way to realize AI and focuses on training machines to learn from multiple data sources to solve complex problems in a replicable, scalable manner. In other words, machine learning is where a machine can learn from data on its own without being explicitly programmed by a software engineer, developer or computer scientist.
AI / ML vs Computer Science: Career Paths and Salary Potential
Professionals with broad, versatile computer science and data science skills continue to grow in workplace demand globally. Careers in computer science are expected to grow by +21% from 2021-2031 according to the U.S. Bureau of Labor Statistics, making it one of the fastest-growing and highest demand occupations -- and AI/ML-related occupations are growing even faster. With a master's degree in computer science or data science, students will be able to earn a median salary of $131,490 per year. The national average U.S. salary for a Machine Learning Engineer is $132,600. For AI Engineers, the average U.S. salary is approximately $156,648.
Also, because computer scientists' expertise extends well beyond individual programming languages, they're also well-positioned for the transition to machine learning based algorithms that negate the need for extensive manual coding and human-built software.
What’s the Difference in Career Paths between Computer Science vs Machine Learning and Artificial Intelligence?
Since computer science, AI and ML are core to the digital innovation transforming every industry, you'll find a broad range of tech, engineering and science roles requesting these skills.
Computer science professionals often specialize in a range of fast-evolving AI methods and applications: natural language processing ("NLP"), robotics, deep learning, neural networks, cybernetics, bioinformatics, and more. Machine learning, on the other hand, presents so many short-term, practical applications for companies and organizations, often you'll see these jobs called "applied" AI/ML.
Below is a sample of relevant job titles for AI/ML professionals, as well as careers for those with education and training in computer science.
Job Titles & Salaries Related to Computer Science, AI and ML
|AI and CS JOB TITLES
|NLP Data Scientist
|MACHINE LEARNING and CS JOB TITLES
|AI Engineer or Applied Machine Learning Engineer
|AI/ML (Engineering) Leader
|Product Manager or Product Director, Machine Learning
|AI/ML Solutions Architect
|AI/ML Ops or ML DevOps
|Responsible/Ethical AI Manager
|OTHER COMPUTER SCIENCE-RELATED JOB TITLES
|(Senior) Product Manager
|(Senior) Software Engineer or Developer
|(Senior) Solutions or Applications Architect
|(Senior) Cybersecurity Analyst or Security Architect
|Director, Software Engineering or Software Development
|*Sources: U.S. Bureau of Labor Statistics, Glassdoor, ZipRecruiter, Zippia
AI / ML vs Computer Science: Necessary Skills and Education Paths
To earn an entry-level role in artificial intelligence or machine learning, you'll need at least a bachelor's degree in computer science, with some basic exposure to the concepts of AI/ML and domain expertise.
Skills requested in these job descriptions include:
- Computer Science Fundamentals: Machine learning and artificial intelligence specialists have foundational knowledge of computer science including writing algorithms, understanding data structures, experience with programming languages, and understanding computer architecture.
- Data Science Skills: AI and machine learning specialists need to be able to extract meaningful insights from data, training machines to do the same. It is important to be familiar with hypothesis testing, data modeling, statistics and probability, and optimization methods.
- Math: Mathematics plays an important role in AI and machine learning. Skill in applied mathematics allows you to choose the correct algorithm to use, decide validations strategies, and approximate confidence intervals.
- AI Specialization: Specialized skills in deep learning, dynamic programming, neural network architecture and more can help advance your artificial intelligence or machine learning career.
- Communication and Problem-Solving Skills: AI and machine learning specialists must be able to communicate with teams and think critically as issues arise, working together to solve problems in real-time. Communication and problem-solving skills are key requirements.
- Domain Expertise: Depending on the AI role, some workers may need specific domain knowledge (e.g., biology, geology) and require a strong ability to collaborate with specialists from diverse backgrounds. For example, an AI specialist in healthcare would need to skillfully partner with medical experts who understand how the human body works, applicable regulations, and more.
With a master's degree in computer science or a master's degree in data science with a machine learning focus, professionals can advance their careers to mid- and senior-level positions. These programs will reinforce the math, statistics, algorithms and other AI/ML-specific knowledge required to succeed with confidence.
What Skills are Needed for Broader Computer Science and Software Engineering?
Computer science bachelor’s degree programs typically help you gain expertise in core computer science fundamentals. Entry-level jobs in computer science that require a bachelor’s degree can include computer systems analysts, software developers, web developers, and database administrators.
Core competencies for the roles include:
- Strong Computer and Technology Knowledge: Technology careers require foundational skills that include understanding machine architecture (how software and hardware standards interact) and how operating systems interface between computers and hardware.
- Math: Math is a key foundational element for any career in computer science. A computer science degree will include instruction in statistics, calculus, linear algebra, and other advanced mathematics.
- Programming: You will need to understand different computer languages, algorithms, and data structures.
- Data Analysis: A computer science degree will help you hone data analysis skills, including how to collect, organize and manipulate large amounts of data using databases and other technologies.
- Communication: Success in the computer science field requires collaboration. Effective collaboration requires professional communication skills. A computer science degree will arm you with key competencies like technical writing, presentation skills, and data visualization.
Even with a bachelor's degree in computer science, it can be difficult to break into some of the best and fastest-growing tech companies. A Master of Computer Science can give candidates a competitive edge to help them stand out from their peers. The MCS@Rice program is designed to help you cultivate your talents and interests so you can launch an exciting career that allows you to help solve the complex and challenging problems of today and tomorrow.
Ethical Considerations in CS's Artificial Intelligence and Machine Learning Disciplines
When considering a career in artificial intelligence or machine learning, it’s important to be aware of two dominant ethical concerns.
First, human bias can and does make its way into AI/ML algorithmic models through training data that include subjective human decisions or historical social inequities.
Secondly, growing regulatory scrutiny around the explainability of AI and machine learning models means that computer scientists and ML experts must document how their models work and make decisions in order to maintain trust and transparency.
A Well-Rounded Degree, with Opportunity to Pivot
Rice’s online Master of Computer Science was designed by our world-class faculty who are recognized for their achievements in teaching, their experience, and their thought leadership. Our rigorous, non-thesis curriculum helps you develop practical knowledge and real-world skills that make you well-versed in computer science and in demand by employers. Along with important core courses, you’ll also gain differentiating competencies like:
- Advanced problem solving and the knowledge to solve problems in the most efficient and systematic way.
- Accelerated understanding of multiple programming languages, to enhance your programming language competencies.
- Experience in applying complex software systems in a variety of industries.