Artificial intelligence isn’t the industry for intellectual lightweights.
As a transformational technology that is likely to touch every aspect of the economy and society, it’s a field that needs top thinkers. And as a kind of computation that challenges the outer reaches of our understanding of math, statistics, and learning algorithms, it’s a world where only graduates with advanced degrees can take a seat at the table.
The big AI braintrust rush has left a particular opportunity on the table for anyone interested in exploring the furthest frontiers of artificial intelligence research, though. Big corporations shoveling big money into the AI furnaces has scooped up hundreds of the finest minds from universities and purely academic research institutes.
It’s a better time than ever to pursue a career in academic AI.
That’s created a kind of vacuum that colleges are struggling to fill.
Finding a Path into Academic AI Isn’t Easy
While professors and academic researchers are hard to come by, there’s a massive influx of students interested in the advanced AI degrees they teach. That hiring spree in the private sector has made AI programs the flavor of the month at the few universities that offer them.
This creates a sort of logjam in the pipeline. There’s more demand for graduate degrees in AI than ever—and fewer and fewer people in the academic world qualified to teach them.
Getting into first-rate AI grad programs is intensely competitive, but it’s a bar you must overcome to pursue academic research in artificial intelligence.
This obstacle exists for anyone looking to get into high-level AI research today, however. So many of the steps to getting into graduate AI programs will be the same whether you are headed toward academia or the private sector:
- Bone up on mathematics, statistics, and computer programming skills early and in-depth
- Front-load your undergraduate studies with high-level computer science and mathematics coursework
- Ace your grades in a relevant major (computer science, math, physics, AI, or ML) at the bachelor’s level
While many people aim to round out their curriculum with less challenging courses to boost their GPA levels for grad school applications, that’s a mistake in the AI world. The harder you challenge yourself earlier in your career, the more mastery you’ll develop in core concepts and the easier you will find your advanced studies.
Passing The First Gate: Earning Admissions to a Top-Notch University AI Program
Challenging yourself also helps establish your commitment to AI. That’s something that both admissions committees and future employers will want to see. Computer science in general is at the top of the list for degree majors with low completion rates. In fact, some estimates show that almost 10 percent of students don’t complete their comp-sci degree studies.
Specific data for AI programs doesn’t exist yet, but considering the challenges cited in computer science—lack of required core skills, misperceptions about the industry, and costs—AI has even worse retention.
Showing interest and taking positive steps toward exploring AI is a good way to get the attention of admissions committees at top research institutions.
So showing some grit is a good first step toward getting into a research-focused AI degree program.
If you’re on the path toward research, it’s also a good idea to have a background that shows some interest in those areas of AI. Whether it’s attending conferences, working on projects on your own time, or just keeping up with the latest publications, you’ll help set the stage to get into a first-rate AI research school.
Getting a Job in Academic AI Research Is More a Segue Than a Transition from Advanced Degrees
There’s a clear divide between education and employment in the private sector and applied AI engineering. You go to school, you graduate, you get a job.
For academic research, you’re looking at something that is a more gradual progression. So your time in grad school is a kind of preview of professional research and teaching positions.
You can expect to work just as hard or more so once you are accepted. AI research can be exciting, but it is also a grind in many cases. Grad students start on the low end of the totem pole—digging through backlogs of papers, trudging through training data, studying competing algorithms. Guess who gets coffee for the whole lab in the morning?
But you’ll progressively take on more and more interesting roles. In many cases, these will either have associated stipends or offer a real paycheck… your first paying job, however minor, in AI research.
Part of the Program in Advanced AI Degree Studies Is Essentially On-The-Job Training for AI Research Jobs
Much of the time spent earning an advanced degree in AI is also a kind of preparation for academic and research roles.
For starters, research is baked in to the curriculum of master’s and doctoral programs of all types. In artificial intelligence, it’s almost always cutting-edge research in some of the most advanced theories. There’s no question it’s an exciting time to be in the field. Participating in groundbreaking stuff all but ensures you will have the right credentials to become a professor or dedicated researcher.
The transition from PhD student to professor is often a gradual one.
Grad students are also expected to assist professors in teaching lower-level courses in the field. So you build valuable experience as an instructor even as you are learning yourself. In fact, many students feel they solidify their understanding of core concepts by teaching them to others.
At the same time, these are the years you’ll first see your name starting to show up on published research papers. Those will be critical toward building your credentials to support an eventual professional position in academia.
Looking at the Life of an AI Researcher in Academia
Working in academia and research has a number of differences from the private sector. How you adapt to these is more about style and preference than a value judgement—neither side is better or worse than the other. But some folks take to the challenges of academia better than those of the corporate world and vice versa.
So what does working as an AI educator and researcher in the academic world take?
Get Ready for a Lot of Multitasking in Academic AI Research Roles
While there are some postdoc and research science positions in academia that are purely focused on the problem at hand, most academic jobs have dual purpose: you must educate and enlighten as well as uncover knowledge.
This requires a lot of work entirely outside of pure research. Participating in grant writing, helping with department management, teaching class, advising graduate students, helping to put on conferences, and many other major and minor tasks fall to computer science professors.
Passing Along What You Learn Is an Important Piece of Academic AI Research
While private sector research happens largely behind closed doors, the entire point of the academic world is to educate. So most researchers at universities carry a heavy load of teaching and advocating for AI on top of actually exploring the topic.
While this can seem like a distraction, it’s often a sort of benefit for dedicated AI researchers. Einstein said that you can never truly be said to understand something until you can explain it to a six-year-old. Although your students will be a bit older than that, the virtues of having to develop explanations for complex subjects don’t go away. You’ll find that you have a better understanding of your research subjects simply by having to explore them from other perspectives.
Finding Funding Isn’t Just Someone Else’s Problem for Academic Researchers
In the private sector, it’s typically the business of the executives and management of a company to come up with the funding to keep it going. Whether investment, products and services, or licensing agreements, it’s generally not the researcher’s problem to figure out how to keep the lights on.
That’s not so true in the world of academic research. While professors have great flexibility in figuring out what to explore in AI, they also have to convince people to help pay for that exploration. That can mean:
- Applying for government and non-profit grants
- Convincing university officials to allocate more funding to the department
- Building support within the department for specific projects
An ability to make your case and drum up support is a crucial part of keeping your research projects funded.
Just getting the funding isn’t the end of the story, either. Grants come with terms; you’ll be responsible for documenting your efforts and presenting results as part of the package.
Professional Status Can Be Stressful Even with Tenure
There is something to the old saying “publish or perish.” Research publications in the field of AI have skyrocketed since 2015, rising from just over 1,000 per year to nearly 7,000 in 2021. There’s little question that value and reputation in the academic world are linked to both the rate and the quality of those publications. So finding time to polish your research, write it up, and submit it for publication is definitely a stressor.
It also requires a special kind of mindset to thrive in a field where your daily research often generates more new questions than answers. Exploring the unknown and turning over new stones has to be a sort of satisfaction in itself for academic AI researchers. There’s no guarantee that your next project will be successful, let alone useful in any practical sense.
Instead, professors and pure researchers need to take pleasure in establishing even negative answers. After all, if you don’t figure out what doesn’t work, how will anyone ever figure out what does?
Academic Life Has Plenty of Perks That Come with the Obligations
Working in academia does come with some unique benefits, however.
For starters, professors are paid better than the conventional wisdom suggests: according to 2022 data from the Bureau of Labor Statistics, postsecondary teachers in computer science earn a median salary of $84,760. Also, many schools have excellent benefit packages, including options for sabbatical.
Academic freedom can’t be overlooked as a benefit. Although there are certainly forces that influence what a professor in AI may choose to research, in theory any topic is on the table.
Tenure is another golden token in the academic world that no one in the private sector enjoys. The job security that comes with a tenured position is the envy of any corporate worker who has faced a downturn in the economy or their industry.
Universities Are Hungry for Qualified AI Research Talent
On the plus side, once you get through the crunch of earning your PhD, the world for AI researchers opens up a lot.
Colleges and universities with established AI departments are hungry for qualified professionals. The 2021 Artificial Intelligence Index Report found that the share of newly minted AI PhD graduates going into academia dropped to only around 23 percent.
All kinds of basic AI research in the academic world is open to anyone interested in picking up the baton. From cutting-edge looks at human computation augmenting AI systems to algorithmic game theory, there are all kinds of areas where much progress remains to be made.
It’s also worth noting that picking an academic and research career in AI doesn’t really rule out private sector pursuits. Many professors in the field also work as consultants, opening up income and applied AI work on top of their theoretical pursuits.
Picking a Path in Academic AI Research Isn’t a One-Way Road
The artificial intelligence field is in great flux today. That’s leading to a lot of back-and-forth movement between academia and the private sector. AI experts are first enticed by big salaries of the private sector, and then enchanted by the freedom and openness of the academic world.
Some applicants arrange to take positions as professors with a delayed acceptance date, allowing for a year or more to work in the private sector before taking an academic position… a so-called prebbatical.
It leads to some pretty convoluted CVs, but this cross-pollination is actually a great thing for both worlds, and for the overall progress of artificial intelligence in general.
It also means that picking the path of an academic AI research professional isn’t a permanent state of affairs. Your advanced skills and knowledge will continue to be a draw for companies who are investing in AI. And universities aren’t going to make up for the gap in qualified professors anytime soon. You’ll be in a great position to enjoy the best of both worlds—academic or private sector work, as each suits your needs or interests.