If you’re considering pursuing higher education in the emerging field of artificial intelligence, you’ve likely heard the phrases artificial intelligence (AI) and machine learning (ML) used interchangeably. While the two are related, there are key differences that students should be aware of if they’re considering a career in this field.

In addition to explaining the differences between AI and ML, this article will provide an overview of career opportunities in these fields and what educational opportunities students should pursue if they want to work in AI or ML.

What Is Artificial Intelligence?

Artificial intelligence (AI) refers to computer software that enables machines to mimic human cognitive functions, allowing them to perform complex tasks, such as decision-making, data analysis, and language translation. Devices powered by AI can learn from interactions and use that data to adjust their responses and performance.

The term is also an umbrella term for training machines to function like a human brain. Many of the world’s largest companies, including Microsoft, Amazon, and Google’s parent company, Alphabet, are investing in AI research and development to improve the technology and find new uses.

Some standard job titles for individuals in the AI industry include AI engineer, data scientist, statistician, and computer scientist.

There are several ways that AI-powered machines can intake information. These methods comprise specialized AI-related sub-fields, including natural language processing (NLP), deep learning, robotics, and machine learning.

What Is Machine Learning?

Although it’s often used interchangeably with artificial intelligence, machine learning is an AI sub-field. Machine learning is one specific method by which AI-powered programs and machines learn the information they use to improve their functions and responses.

ML uses data sets to train algorithms to produce machine-learning models capable of performing complex tasks. In a way, ML teaches machines to teach themselves rather than needing a human to program them. Within ML, there are even more specific types of learning models. For example, ChatGPT, the free online AI chatbot, is powered by an ML method known as a Large Language Model (LLM), which allows the bot to process vast amounts of text data and infer relationships between the words.

Individuals who like working with data may want to explore ML as a career option. ML uses data, including numbers, text, or photos, to train devices to recognize patterns or make predictions.

Why Choose a Career in AI or ML

According to the U.S. Bureau of Labor Statistics (BLS), AI-related jobs are some of the fastest-growing fields in the nation.

For data scientists, who are integral to ML, the BLS predicts a 35% increase in job openings through 2031. The agency estimates there will be 17,000 new job openings yearly for data scientists. This position is also lucrative, with a median annual salary of $100,910.

The BLS also anticipates faster-than-average job growth for other AI-related jobs — computer and information research scientists. Individuals in this field can expect around 3,000 new job openings annually through 2031, representing a 21% increase in employment opportunities. According to the BLS, computer and information research scientists earn a median annual salary of $131,490.

The rapid expansion of AI into various fields, including healthcare, business, retail, and more, means AI and ML engineers will have multiple opportunities in niche industries in the coming years.

Beyond practical considerations like job outlook and salary, AI and ML are ideal careers for individuals who like being at the forefront of technology, enjoy working with data, algorithms, and software, and have aptitudes for problem-solving, mathematics, analytical thinking, and collaboration.

Preparing for a Career in AI and ML

The exact education requirements for jobs in AI or ML depend on the specific position. However, it’s common for employers to seek individuals with a bachelor’s degree for entry-level positions and individuals with a master’s or Ph.D. for more advanced positions.

While there are some bachelors-level AI or ML degree programs, students typically earn degrees in computer science, mathematics, or statistics at the undergraduate level. These degree programs offer students foundational knowledge in concepts and skills related to AI and other computer science-related areas, which can be ideal for students who want to explore AI before fully committing.

Because of the advanced nature of the field, it’s more common to find specific AI or ML degree programs at the master’s level. These degree programs allow students to work with experts on new, innovative technology to learn the most current skills and concepts.

An alternative option to pursuing a degree in AI or ML is to enroll in a certificate or bootcamp program. These educational programs typically take less time to complete than a full degree and may be cheaper and more flexible. Certificates and bootcamps may be worth considering if you want to test-drive AI or ML before committing to a full degree program. However, Students should note that having a degree may make them eligible for a broader range of job opportunities and higher starting salaries.

Read more about How to Become an Artificial Intelligence Engineer.

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