AI Degrees and Certs to Build Next-Gen Artificial Intelligence

If you’re not already tied to the computer science world and tapped into the underground buzz, the sudden appearance of AI (artificial intelligence) and ML (machine learning) in products and headlines might have come as a surprise. After the release of OpenAI’s ChatGPT in November of 2022, suddenly it seemed like AI was going to change everything, for everyone.

But AI didn’t come out of nowhere. And while it is going to change everything, it’s not going to do it by itself.

Where it came from was decades of both pure and applied research in computer science departments and technology businesses. Pushing forward the state-of-the-art, graduates with the math, programming, and data skills that come with compsci degrees have been on the front lines of AI.

That same group is going to be leading the charge into the unknown frontiers of machine intelligence for decades. Only now, specialized degrees exist to equip them with the right knowledge and skills for the job.

How Do You Find Degrees in AI That Are Focused on Development and Pure Artificial Intelligence Research?

Computer science is the umbrella field that artificial intelligence and its tributaries join within. As the study of computers and computation, it’s the core of AI research and development… even as that R&D expands and branches out in new directions.

So you’ll find a variety of different types of degrees that live under the artificial intelligence label. It’s best at this point to think of them as tendencies rather than hard divisions. There is plenty of overlapping coursework, and graduates with advanced degrees in any of these areas won’t have much trouble qualifying for almost any job in the industry. In addition to those focused on computer science, there are also:

It’s also important to understand that the divisions aren’t necessarily between degree programs themselves. The same degree might offer a concentration option in machine learning and deep learning, squarely in the hardcore comp sci field, and another in computer vision and robotics, which is a more specialized area of applied AI engineering.

All of those are equally important to AI development and adoption in the long run. Yet the most exciting parts of computational intelligence are still being explored squarely in the realm of computer science.

AI degrees aimed at computer science professionals tend to be among the purest in their approach to investigating artificial intelligence theory.

These degrees are taking on the hardest problems in the field. Students in these programs are part of research efforts digging into topics like:

Integrated circuit design for advanced AI computing applications
Flexible and deformable robotics design
Sensor fusion
Distributed AI systems
Combinatorial problem solving
Geo-spatial visualization
Knowledge representation and efficient search and deduction

With every new discovery and innovation, they could open up entirely new specialties in AI almost overnight.

Career Paths for Computer Science-Focused AI Degree Graduates

Graduates from these programs are in high demand at big name companies like Apple, Google, and IBM. They also have an in at hot startups like OpenAI or Elemental Cognition, which are moving fast and building cutting-edge tools based on their own innovation and research.

The pace of innovation in this aspect of AI is really outstripping any common terms or definitions. You will find positions with a wide array of titles, including:

Artificial Intelligence Engineer
Machine Learning Engineer
Data Scientist
Research Scientist
Research Engineer
AI Software Engineer
Software Architect
Developer

In many cases, these just bleed over into AI positions from longstanding roles with the same name at the same companies. At this stage, there is no universal consensus on what duties or tasks in AI are associated with particular titles.

If you look past the labels, however, you’ll find the roles clustered around three basic functions. Each of those has various applications in different types of AI development and research.

Research

Researchers are immersed in the math and logic that powers AI systems. They pioneer new algorithms, optimize existing systems, and explore new ideas for powering machine reasoning and problem solving.

While some positions in AI research may operate purely in the theoretical world, there’s often a particular problem or focus they are aimed at:

In every case, the job involves both individual research and high levels of collaboration. Scientists keep up with the latest developments and publications and work to keep their efforts in line with current models and understanding. They often present their work to peers in and outside their company, putting it out there for other minds to poke holes in it.

They design experiments and write code and tests to develop a better understanding of the problems and to attempt solutions.

Design and Development

Software architects and engineers have a more focused role in AI development. Their job is to take theoretical breakthroughs and turn them into useful tools, even if it’s at a very high level. They’ll take algorithms from scientists and crunch terabytes of data through other train models. They’ll write the code for API (Application Programming Interfaces) to those models or to integrate sensors and systems with AI.

To do all that, they need a broader understanding of the tools being developed and how to build systems around them. They’ll consider issues like reliability and accessibility.

That means collaboration with testers and other engineers. A lot of code writing and revision happens at this level. It’s the first level of implementation for many AI systems, and there is a lot riding on getting it right.

Testing and Quality Assurance

In a field that remains full of unknowns, testing and quality assurance is turning out to be a very big role, indeed. When AI is hallucinating, or starts spewing racist memes, test and QA engineers are on the spot.

This position involves writing a lot of code and creating automated testing environments. These jobs may involve the critical role of creating training data and supervising machine learning for algorithms that are being polished to perfection.

It also involves attempting to understand the reasoning and logic behind the operation of the models. Some of the biggest mysteries in AI revolve around how to assess its ability to perform reliably in important roles. Digging into the operation and safeguards of the systems is the only way to assure companies and individuals that the tools are fit for the job.

In every case, the positions are par for the course for tech companies. You can expect a casual working environment, plenty of resources to get the job done, and an exciting, tough, and demanding atmosphere in which to work.

What Kind of Companies Employ AI Scientists and Researchers?

Graduates with these degrees can move into almost any field that needs advanced AI development and engineering. But they will find the greatest demand for their skills with the fast movers of the AI industry:

There’s a spectrum of projects and responsibilities that run through these organizations. The purest R&D work will be happening at the non-profit and academic end of the field. Startups are looking to challenge the status quo with more far-reaching and more innovative projects than their big competitors. And those competitors, the behemoths who already have massive customer bases and established products, are most likely to have you diving into AI applications and improvements to their existing offerings.

Although the companies may be public facing, these are the types of positions that are putting the magic together behind the curtain.

What all of these potential employers have in common is a drive to enhance the fundamental capabilities of AI. They’re not sitting back and waiting to see what happens in the field. They are putting significant investment into hiring the best and the brightest to make sure the breakthroughs are happening with their brand name on the label. Expect fierce competition and some secrecy to surround those efforts.

Both Salaries and Job Prospects Are Hot in AI Development Today

Except for academia, most of these organizations either have deep pockets or the prospects for big profits later. And that means the kind of compensation that you can get for a comp-sci focused AI degree is substantial.

AI is too new to have its own job categories assigned by the Bureau of Labor Statistics (BLS), the government agency responsible for collecting salary and employment information.

Instead, the positions listed above will tend to fall into one of these existing categories, shown here with their respective salary rates for 2022 (90^th percentile):

Researcher
- Computer and Information Research Scientists - $232,010
- Data Scientists - $174,790
Design and Development
- Computer Hardware Engineers - $208,200
- Computer Programmers - $157,690
Testing and Quality Assurance
- Software Developers, Quality Assurance Analysts, and Testers - $159,740
- Computer Systems Analysts - $161,980

Obviously, because these positions are so new, you have to take the salary information with a grain of salt. There are few AI-specific jobs in any of those categories yet. And many of those may be so new they aren’t even showing up in 2022 data!

But those professionals aren’t coming out of nowhere; they were working in those or similar job categories previously and expecting salaries within a similar range. Because the very best are being recruited for these key roles, you can expect the rates to be higher than average. We’ve listed the numbers for the top ten percent of those positions to get into that ballpark.

Because they are working at companies in an industry known for great benefits and golden handcuff treatment, you should also expect plenty of amenities and extras on top of the base salary. Stock options are common; services ranging from free food to getting your laundry done at the office are all on the menu in the tech world. And where speed is a key competitive advantage, bonuses are likely to be on the table.

The Special Elements That Go into a Computer Science-Focused AI Degree Curriculum at Every Level

To get into these pinnacle positions in AI, you’ll need the hardcore curriculum that comes from this sort of degree: fundamentals in math, algorithmic design, the processes of deep learning. You’ll get a lot of directed experience in programming AI systems, learning how to lay down the code that anchors the thoughts of the machine.

Not much of these comes into play at the Bachelor of Artificial Intelligence level, however. Bachelor’s degrees that lead to this career path will have to include a lot of strong math and science preparation. Coding skills will be important, as will any statistical or data science courses you can lay your hands on. But you won’t find a clear delineation in four-year degrees between the three major career paths.

It’s really the Master of Science in Artificial Intelligence where the career track tendencies emerge. For a master’s in AI, or closely related programs like a Master of Science in Machine Learning, you’ll find coursework that builds on those math and science skills like:

Fundamentals of Artificial Intelligence
Machine Learning and Deep Learning Algorithms
Reasoning and Logic for Machine Intelligence
Data Science for Machine Learning
Human-Computer Interaction
The Ethics of Artificial Intelligence

With only two years to dive into these advanced skills, you’ll have to narrow your specialization. You’ll also build research and experimental skills through a capstone or thesis project… vital in computer science AI careers.

Those same themes are pursued at the PhD in Artificial Intelligence level. As the most advanced studies available in AI today, these degrees commonly take three years to complete. You’ll spend much of that time conducting unique research in your dissertation topic, pushing the boundaries of knowledge in the field into the future. Your coursework will help you achieve that goal, with classes in subjects like:

As with a master’s degree, a PhD in AI and Machine Learning comes with a lot of room for individual focus. So you can get as theoretical and detailed as you like in the fundamentals of the field.

There are fewer graduate and postgraduate certificates in artificial intelligence that are aimed at computer science professionals working on the big problems in the field. As much shorter educational programs, these only have a year or so to hammer in some pretty advanced material. That’s often not enough for the full menu of skills and knowledge needed to work on AI in compsci.

But some AI certificate programs, like an Advanced Graduate Certificate in Artificial Intelligence, are intended to build precisely those skills, designed for graduates who earned a master’s in computer or data science before AI was even a real degree option. In those cases, you’ll find a curriculum that takes the advanced concepts you learned in programming and data and builds them out with studies in NLP or deep learning.

For the Best Education, Seek Out Schools at the Cutting Edge of AI Research

There aren’t a lot of choices for AI programs in general yet, but that’s changing fast. More and more schools are getting into the game with degrees suitable for building a career in AI technology development.

But how can you tell which school is right for you? One that not only delivers a degree in AI, but focuses on the kind of cutting-edge, development-oriented education you need for careers in major AI dev shops?

In some cases, you’ll find that the school that offers these degrees has much to do with how computer science-oriented they are. In some cases, the same university might have a School of Engineering and a School of Computer Science that each offer a master’s in AI… you can guess which is more appropriate for this career path.

In other cases, you might see these degrees offered by an engineering department, but from a school that is known for cutting-edge research in AI theory. Those might also sometimes be an excellent choice for honing your core R&D skills in the field, too.

Ultimately, you’ll know what you’re getting by taking a close look at the curriculum. But you will also want to dive in to check all the other factors that deliver an elite education in computer science-focused AI.

Finding the Qualities You Need in Computer Science-Focused AI Degrees

Of course, a challenging curriculum is one excellent sign of what you are getting into with an AI education. And there are more clues to run down that flags these schools, such as:

Active research projects in cutting-edge areas of AI development
Major partnerships with industry leaders in AI
Professors who are widely published and well-respected in AI research
Labs and learning resources that cater to the demands of AI-education, like serious cloud computation resources or labs focused on hot areas like NLP
Internship opportunities in fast-moving AI projects
A track-record of placing alumni at big players in the AI field

These things tell you you’re on the right track for a degree to support leading AI development careers. But there are other factors in play that go into making it a great one…

First-rate academic advising will help you put together the right lessons plans for success in a fast-changing industry
Strong research support through extensive collections of the latest journal publications and library professionals to help you sort through them
Interdisciplinary programs that bring the best of math and electronic engineering expertise to bear on your studies as well

More than any other AI career path, scientists and pure researchers begin their work while still students. Much of the basic and essential research happening in the field is still coming from academia. So when you’re picking your school, your degree program, and your research subjects, you’re already well into the work of building next-gen AI systems.

2022 US Bureau of Labor Statistics salary and employment figures for Computer and Information Research Scientists, Computer Hardware Engineers, Computer Systems Analysts, Data Scientists, Computer Programmers, and Software Developers, Quality Assurance Analysts, and Testers reflect national data, not school-specific information. Conditions in your area may vary. Data accessed December 2023.