Before machine learning ever became a thing, it was preceded by an awful lot of old-fashioned human learning. The same is true of artificial intelligence. The very concept of machines that think, let alone all the painstaking research and programming that has led us to the doorstep of true AI, is entirely down to hard academic work done by people like you.
The long path to the development of artificial intelligence is paved with the hard work and education of human intelligence.
That makes degrees in AI and ML not just a necessity for anyone who wants to build a career as an AI engineer, but also a must for the advancement of the field itself. A lot of tomorrow’s tools and techniques for AI programming are being dreamed up by students in AI degree programs today.
The Rise of AI Degree Programs: A New Frontier of Knowledge Requires a New Category of College Degree
Most of the work that has been done in AI, and that’s still being done today, is owed to talented individuals who don’t have degrees in AI at all.
Artificial intelligence as a separate college major is a relatively new innovation. For decades, AI was seen as a sort of backwater in the field of computer science. So anyone interested in working in AI had to make do with degrees in fields like:
- Mathematics
- Physics
- Computer Science
- Software Engineering
- Statistics
- Data Science
With the unveiling of amazing generative AI tools and new possibilities in everything from robotics to weather forecasting, universities have realized that AI is a field that requires its own specialized interdisciplinary studies. Combining the fields above, these are the degrees that are preparing the AI engineers of today and tomorrow.
Artificial Intelligence vs Machine Learning Degree Programs
Although research has been happening for decades in artificial intelligence, degrees in the field are a new development. The educational system is still sorting itself out, as is the job market for AI professionals.
Already, however, you can see a few different kinds of groups forming in AI degree programs.
The first big split is between artificial intelligence and machine learning degrees. Although the headlines routinely use AI and ML interchangeably, the fact is that AI describes the more general field, with machine learning being a specific technique used in both AI and elsewhere. Data science, cybersecurity, and marketing are all industries that rely heavily on ML outside of the AI context.
But most ML programs are still applicable in AI. In fact, there is no shortage of degrees in AI with a strong focus in machine learning. You’ll find that a Master of Science in Artificial Intelligence and Machine Learning is nearly as common as a straight Master of Science in Artificial Intelligence. Still, ML is a specialist approach and selecting a degree in this area will certainly focus your AI engineering career.
For a full and nuanced explanation, see our guide offering a detailed break-down of the differences between AI and machine learning.
The Demands of Different Industries Also Influence the Type of College Degrees Offered in AI and ML
AI degrees are increasingly dividing along three primary career paths that are emerging out in the real world. They aren’t hard and fast categories. Most degrees in AI or ML will give you the essential knowledge to get started down any path. But you will find that degrees are being developed to expose you to the most relevant research based on the path you intend to take, offering classes tailored to the daily challenges of different kinds of roles.
What exactly are we talking about here? You have degrees designed for those who will be working to develop and advance AI, then you have degrees for those who will simply be working with AI tools in different fields…
- It can lead to jobs like AI Research Scientist, Deep Learning Performance Architect, and Research Engineer for Superalignment are likely to fall into this bucket.
The second path is for those exploring applied uses of AI tools and concepts in standard business and government operations. These programs take a broader perspective on AI, looking at how it connects to economic and social trends. You’ll learn more about putting existing AI tools and core AI engines to practical use, and how controlling and regulating those systems will optimize and improve the ordinary functions of business and government, from managing supply chains to handling customer service.
- It can lead to jobs like AI Solutions Engineer, Generative AI Engineer, Prompt Engineering Supervisor, and Expert AI Animator are examples of what you will find down this path.
The third path is that of highly specialized, technical applications of AI in specific fields of expertise. These are degrees that are intended to connect the latest developments in AI to career fields that are already highly specialized, like medicine and engineering, among dozens of others. Though for the most part, these types of degrees are very much in the development stages at this time, you need only to look to the ways AI is already shining brightly in fields as diverse as medical diagnostics and finance to know they are coming. The coursework in these programs puts together the specific challenges involved in those industries and explores how AI can best be used to solve them.
- It can lead to jobs like AI Inference Engineer, Healthcare AI Engineer, and Engineering Director for Embodied AI are some that you might find in this category. Soon many standard job titles in fields like healthcare and engineering will also include working with AI tools and concepts.
Naturally, the salaries and job titles you will find will vary across these categories. Our AI career guide will help you drill down into the details.
AI Degrees at All Levels: How Learning Artificial Intelligence & Machine Learning Differs for Undergrads vs Graduate Students
Unlike many other fields, AI is complex enough that you will see significant differences in curriculum at different degree levels. There are core skills that you must master to survive at the next level. These are sometimes in entirely different fields, such as mathematics and statistics.
While a math major, though, will study mathematics at the bachelor’s level and simply go on to study more advanced permutations as a master’s and then doctoral candidate, an AI major will learn the essentials as an undergrad, and then go on to apply it toward different topics completely as a grad student. Calculus, for example, will support statistical analytics at higher levels; programming will feed into classes for algorithm development.
Most coming into the field right now with an interest in the development and advancement of AI technology are building on existing undergraduate and graduate degrees in computer science, software engineering, or data science, among the other fields mentioned above. AI is certainly not a field of study where you would expect to take a stepladder approach, moving from undergraduate to master’s and doctoral degrees. The education the field requires isn’t that prescriptive, and even if it was, the degrees simply haven’t been around long enough for professionals entering the field to have been able to move up from undergraduate to graduate studies solely within the AI vertical.
Still, even in these early days of the AI revolution, degrees in artificial intelligence are already available at every one of those levels, catering to eager students at every stage of academic preparation. As you would expect, graduate degrees and certificates were the first to proliferate, and since, associate degrees and even undergraduate certificates have begun to emerge.
Two-year programs that cover the basic concepts and offer a start on the advanced math and science training needed to progress in AI. Few positions in the field today are accessible with an associate degree. Instead, AS degrees are often used to prepare for a bachelor’s program and may transfer across to fulfill the first two years of bachelor-level degree studies.
Core skills learned:
- Basic math
- Introduction to programming
- Elementary AI and ML theory
- Essential liberal arts and general studies
Four-year bachelor’s degrees in artificial intelligence come with a much stronger education in math and science, as well as introductory coursework in ML and AI development. Programming skills and advanced math and statistics are taught as a foundation for further study. Broader liberal arts courses help with communication, ethics, and creative problem-solving skill development. Still, few positions in AI today are available with only a bachelor’s program, so most students will go on to graduate studies.
Core skills learned:
- AI and ML theory and systems
- Advanced calculus and statistics
- Expanded liberal arts studies
- Software engineering skills and practices
- Specialist courses in major are like algorithm design, robotics, ML training methods, data structures, and cognitive psychology
Arts Rather Than Science Focused AI Undergrad Degrees Are Rare but not Unheard of… And May Even Come with a Strategic Advantage
It’s rare, but possible to find AI majors in a Bachelor of Arts rather than a Bachelor of Sciences degree. Science-focused degrees have fewer humanities courses and delve deeper into engineering and math; BA degrees deliver a few more traditional liberal arts courses.
You’ll also see Applied Science or Applied Technology (as in an Associate in Applied Sciences in Artificial Intelligence and Machine Learning) from time to time. These are considered even more technically focused than regular Science degrees, delivering coursework that is almost entirely job-related.
While extra engineering knowledge will certainly help you uncover the nuts and bolts of how machine intelligence operates, it’s not always the right choice for every career path. That’s because AI is bound to have significant social and humanitarian impacts on the world. It may well be that understanding how the technology fits into the big picture trends of human historical development will be every bit as important in some roles as a solid technical understanding.
That means you might elect to seek out an arts program for your AI education. Even machines that can speak for themselves will need a translator to the world of humans from time to time. Leaders and visionaries in AI will need the broader education to tie their engineering ideas into culture and society.
Two-to-three-year master’s degrees in artificial intelligence finally offer a real focus on AI and ML development skills. With the basic programming and math skills already in place, you can focus on research and applications of those skills in AI. Graduates at this level provide the bulk of the workforce in real AI engineering roles today.
Core skills learned:
- Advanced theory and extended AI research
- High-level statistical calculation and advanced moths
- Computational theory and analysis
- Ethics of AI
- Deep Learning and Advanced Neural Network design
- (As applicable) Highly specialized topics within the focus area, specifically machine learning, computer vision, or natural language processing
A three-to-five-year doctoral degree in artificial intelligence is the highest you can climb in AI and ML education. Whether as a PhD in Artificial Intelligence or a Doctor of Engineering in Machine Learning, these degrees produce the experts who guide the industry with new research, advanced techniques, and insights into machine intelligence.
Core skills learned:
- Anything you want… doctoral students have wide latitude to select advanced research and development areas in AI and machine learning. They are responsible for designing and performing experimentation and designing a curriculum to support their area of investigation.
AI has gotten off to a fast start in recent years. For many existing computer science, math, and other STEM graduates, it was too fast to get a degree in the field. But with the same hard sciences background, it is possible to get up to speed with short post-bachelor’s and post-master’s certificate programs in artificial intelligence. Focused entirely on AI or ML, or sometimes on specific applications, these require you bring the right training and experience to the classroom to keep up.
Core skills learned:
- Advanced theory and extended AI research
- High-level statistical calculation and advanced moths
- Computational theory and analysis
- Ethics of AI
- Deep Learning and Advanced Neural Network design
- (As applicable) Highly specialized topics within the certificate focus area, i.e. machine learning, computer vision, or natural language processing
Preparing to Earn Your Degree in Artificial Intelligence
Not only will you have to explore the factors of career path, degree level, and curriculum being offered, but you’ll need to establish the overall quality of the department offering the program and the college or university itself.
Not all schools offer degrees in every type and level. Among those that meet your basic criteria, though, you’ll also want to look at important factors like:
- Instructor quality and expertise, both in terms of academic and professional experience and reputation
- Current research projects and publication history, reflecting projects you are interested in pursuing
- Academic and research support services, including cloud computer capacity and grant writing support
- A strong reputation in the academic and professional AI community, with solid ties to both companies active in the field and other researchers
With a lot of room for discoveries in AI still remaining, you’ll notice that research remains a hot topic. It can pay big dividends to select a school that has a reputation for research and breakthroughs in the particular technique (machine learning, computer vision, NLP, etc.) and industry application (medical diagnostics, engineering, autonomous vehicles, etc.) that aligns with your career goals.
You’ll certainly benefit from being within the orbit of professors whose names are going into the history books as the prime movers of next-gen AI technology.
Earning an AI Degree is an Investment in Your Career
AI degrees don’t come cheap. With an extensive, and often graduate-level, education required, you’ll be spending a lot of years in classrooms. And as we are all aware, the cost of college has been rising for decades. You’re going to be signing over a healthy check to any school for every one of those years it takes to get your education where it needs to be.
But not every degree level or school will have the same price. The National Center for Education Statistics tracks data on college costs in the United States. According to their breakdown for 2022 (or 2021 for graduate cost data), the average price of tuition and fees per year comes out like this:
- Two-year schools (associate degrees, undergraduate certificates) - $3,859
- Four-year school undergraduate programs (bachelor’s degrees, some associate degrees, undergraduate certificates) - $9,596 public / $34,041 private
- Four-year school graduate programs (master’s and doctoral degrees, post-graduate certificates) - $12,394 public / $26,621 private
On top of that, you’re going to be facing costs for room and board, study materials, and various incidentals… in particular, graduate students may want to attend conferences, hire professional editors for journal submissions or thesis papers, and are likely to accrue miscellaneous expenses relating to research projects.
Fortunately, loans are widely available at all levels of education in AI. As you progress toward the doctoral level, you will also find that some schools go out of their way to help strong candidates cover the costs of attendance. That can come through grants, paid graduate teaching positions, or fellowship programs.
Online Degrees and Certificates Are Standard in AI Education
Many AI degrees, particularly at the undergraduate and master’s levels, and almost all certificates, are available online as well as in traditional on-campus formats. You can get just as much out of your studies with your laptop at your local coffee shop as at the university student union.
Even better, online options will completely change the range of different schools you can consider. When you don’t have to worry about moving halfway across the country, suddenly many programs look more accessible.
Doctoral programs are less likely to be available entirely online. But they do come with a great deal of independence built in that may allow off-site study for part of the program.
With asynchronous classes, which don’t require you to attend at any specific time, your options grow even further. Particularly in graduate-level studies, this can let you put together your own schedule to accommodate work or family obligations.
And if some of those tuition prices listed above gave you sticker shock, online programs are one way to keep those costs in check. By sticking close to home, you can keep your room and board costs manageable. You also don’t have to worry about additional impacts from relocating or commuting to class from day to day.
One thing you can be sure about is that there will be plenty of people willing to do the work and pay the price to earn these degrees. It’s never been more clear that the future will be forged by people with advanced education in artificial intelligence. These programs are the only way to get there.