More and more CS students are interested in AI – and there aren’t enough lecturers

Computer-science departments across US universities do not have enough lecturers to teach increasing numbers of students interested in AI, a report from the Center for Security and Emerging Technology (CSET) this month suggested.

Interest in machine learning and artificial intelligence has risen and fallen since the field was formally founded in the 1950s. Neural networks have made a comeback in recent years, exploding in popularity with deep learning. Demand for machine-learning courses at universities has skyrocketed, we’re told, and there aren’t enough lecturers to support students’ interest.

Data compiled by the Taulbee survey, and quoted in the report, showed that between 2011 and 2020, the number of students enrolled in computer-science programs in America tripled from 60,661 to 182,262. But the number of faculty in computer-science departments increased under 1.5X from 4,363 to 6,230. The aggregate student-to-faculty ratio across the surveyed departments doubled up from 14-to-1 to 29-to-1. 

Now, to be clear, these are stats represent all students enrolled across 140 US computer-science departments, rather than those students specifically signing up for AI classes, though the report argues the figures are indicative of a rise in interest in courses that primarily drive the teaching of machine learning. The executive summary concluded:

While there is an undeniable rise in those taking computer science, you have to take the report’s word for it that this in all probability equates to a rise in interest in ML. “Many AI courses are taught in computer science departments, and AI specialists account for a growing portion of CS faculty overall,” the report noted in an appendix.

Some universities have had to cap the number of students for particular classes due to a lack of teaching staff. Limiting education will have a detrimental effect for the United States, the report’s authors Remco Zwetsloot, a fellow at the International Security Program at the Center for Security and International Studies, and Jack Corrigan, a research analyst at Georgetown’s CSET, explained.

“Teaching capacity gaps limit the amount of talent flowing into the US AI workforce, which in turn negatively impacts economic and national security,” they wrote. “Research has shown innovation is partly a function of the absolute number of researchers in a particular field and the act of generating new ideas is becoming more labor intensive. Less talent therefore means less innovation.”

Experts in AI have previously warned that universities are suffering from a brain drain of talent. Instead of going into academia, they’re moving towards research positions in industry due to higher salaries and access to better resources, leading to fewer tutors in colleges.

But Zwetsloot and Corrigan believe the data shows this isn’t the whole picture. It’s not that universities are struggling to hire faculty to support more students, it’s that they aren’t hiring at a fast enough pace. Some academics wooed by industry often continue to stay at their departments and only spend 10 to 20 percent of their time working for a company. 

“We found little evidence to suggest the outflow of AI faculty from academia to industry has increased in recent years, and, though a larger share of new PhD graduates is indeed taking jobs in industry, survey data does not indicate that they are disinterested in academic careers. However, we did find evidence that suggests universities have not increased the number of computer science faculty positions in line with the growing demand for AI-related education,” the report said.

Percy Liang, an associate professor of computer science at Stanford University, however, told us: “It is true that the number of available faculty positions has not grown nearly as quickly as the number of industry positions, but I think brain drain is real: researchers choose industry over academia or leave academia to go to industry because of higher compensation, more data and compute.”

Zachary Lipton, an assistant professor of machine Learning and operations research at Carnegie Mellon University, meanwhile, told The Register he doesn’t see a huge brain drain of researchers going into industry. After working for a couple of years for a company, many often return to academia.

“Yes, there is more pay in industry but it’s boring in a sort of way,” he told us. “Their focus is more myopic. There are more important interesting problems in foundational, theoretical research that are still best studied in academia.”

Lipton said the surge of interest in machine learning is for introductory courses that cover the basics, and these classes are useful for a wide range of careers outside of academia. There isn’t as much demand for advanced graduate-level study. To cope with the increased demand, universities should boost teaching faculty rather than researchers seeking tenure. 

“Universities should make the teaching track more attractive,” he told us. “These faculty members don’t have to worry about grants or running a lab, but it’s very hard to accept a pay cut just to focus on teaching. Tenured academics may teach some introductory courses but their primary focus is research. We need to find more people that have a passion for teaching, who are able to connect with a broad base of students.”

The report suggested the US government should step in and increase funding for universities so they can hire more faculty. There should be more options outside of academia for people to upskill and take these introductory to AI courses at community colleges or online. The private sector can help, too, by donating to universities, continuing to fund grant awards, and supporting new academic posts. ®