The Artificial Intelligence Education Guide

Why Study Artificial Intelligence?

“Artificial intelligence (AI) could be the most transformative technology in the history of mankind.”

You’ve heard the hype. We’ll give you the facts. Yes, AI is a transformative technology. But it’s moving at supersonic speeds. Today it’s automation, robotics, driverless cars, AI assistants, 3D holograms, and individualized online & media experiences. Tomorrow it will be things we have yet to dream of.

Which begs the question… Is a master’s degree in artificial intelligence worth the money? After all, AI has been through boom & bust cycles before. You could decide to invest in an academic qualification. Or you might be better off pursuing relevant certifications & training on the side.

We’re going to let you decide. Explore our degree guides, rankings, and FAQ to learn more about the actual content of a master’s program in AI, machine learning, robotics, or computational linguistics. Follow the links in the listings to decide if coursework is relevant and AI faculty are up to scratch. Find out what’s happening with AI in a wide array of industries.

With the right professors and research opportunities, a graduate degree in AI has the power to transform your career. Our goal is to ensure that you know all of your options and go in with your eyes wide open.

The History of AI Degrees

The Birth of Artificial Intelligence

Most historians point to 1956 as the genesis of AI as an academic field. That’s when the modest Dartmouth Summer Research Project on Artificial Intelligence was convened in New Hampshire. John McCarthy, a brilliant young Assistant Professor of Mathematics at Dartmouth, was one of the key organizers.

It’s the same workshop in which Allen Newell, a bespectacled Ph.D. student at the Carnegie Institute of Technology, and his advisor, Herbert A. Simon, presented a new computer program that they had created with Cliff Shaw, a systems programmer at the RAND Corporation. It was called the Logic Theorist.

All Hail the Technical Ivies

The first AI boom had begun. During the 1950s and 60s, it was the “Technical Ivies” that led the way in education and research:

• MIT: In 1959, McCarthy and Marvin Minsky (formerly of Harvard) founded the Artificial Intelligence Project at MIT. It eventually merged with Computer Science in 2003 to become the world-renowned Computer Science and Artificial Intelligence Laboratory (CSAIL). MIT was also the home of the Project MAC, funded by DARPA and launched in 1963.
• Carnegie Mellon: Meanwhile, over at Carnegie Tech, Newell and Simon were busy founding their own AI lab and working on theories & programs like 1957’s General Problem Solver. In 1979, Carnegie Mellon forged ahead and launched a dedicated Robotics Institute.
• Stanford: By 1963, McCarthy had become a full professor at Stanford. It’s thanks to him that the Stanford AI Lab (SAIL) has now become famous for its work in speech recognition and robotics. SAIL was one of the first ARPANET sites and gave birth in 1969 to… SAIL, also known as the Stanford Artificial Intelligence Language.

PhD Graduates Make Their Move

These schools weren’t the only ones involved in pushing the boundaries of the field. At Cornell, Frank Rosenblatt published one of the first papers on the perceptron in 1958. In 1966, code-breaker Donald Michie spearheaded efforts to develop a Department of Machine Intelligence and Perception (DMIP) at the University of Edinburgh.

Soon enough, ideas spread to other major universities. In 1974, Stanford alumni Roger Schank and Chris Riesbeck created the Yale AI Project, with a special interest in natural language processing. In 1984, the University of Georgia formed an Artificial Intelligence Research Group—now the Institute for Artificial Intelligence. In 1986, Stuart Russell completed his PhD at Stanford and joined the faculty of UC Berkeley.

By the early 2000s, AI had become an established discipline in academic circles. But it was often relegated to a research group or lab within a Department of Computer Science. You could encounter hordes of PhD graduates who specialized in the field. But undergraduates were thinner on the ground. It would take a perfect storm to launch AI education into the stratosphere.

The Meteoric Rise of AI Degrees

In the 2010s, three forces collided—big data, computer processing power, and improvements in learning algorithms. With advancements in machine learning and deep learning, we went from the Bronze Age to the Iron Age of artificial intelligence. This is the decade of NEIL, Siri & Alexa, ONNX, AlphaFold, Deepfake technology, the Tesla AutoPilot, and Cat vs. Dog. It marks the first time that IBM Watson won Jeopardy.

Universities responded by upping their offerings and refocusing their efforts on industry funding. In 2018, Carnegie Mellon was the first school in the USA to offer a bachelor’s degree in AI. According to Stanford’s 2021 AI Index Report, courses in AI increased by 102.9% at the undergraduate level an 41.7% at the graduate level from 2016-2020. PhD graduates in AI-related fields jumped from 14.2% in 2009 to 23% in 2019.

The most prestigious graduate programs in AI are still to be found at the Technical Ivies, but they now have competition. All kinds of public and private universities have begun to offer programs in AI, including AI bachelor’s degrees, online graduate degrees, MBAs, graduate certificates, and a slew of dual majors. In 2023, the University of Texas at San Antonio even launched a combined MD/MS in AI.

Frequently Asked Questions About AI

On the fence about your decision? Use our FAQ section to make your mind up. We cover the pros & cons of academic programs and provide helpful tips on narrowing down your choices.

• Is a Master’s in AI Worth It?
• Which Degree is Best for Learning Artificial Intelligence?

Forecasting the Demand for AI Experts

AI is developing so rapidly that’s it difficult to predict what tech jobs will be obsolete in 10 years time. However, it’s safe to say that industries and governments are going to be leading the charge for more spending on research & development:

• In 2017, a PwC Global Artificial Intelligence Study suggested that AI could end up contributing $15.7 trillion to the global economy in 2030—$6.6 trillion from increased productivity and $9.1 trillion from consumption-side effects.
• In 2023, a MarketsandMarkets report on the Artificial Intelligence Market predicted that the global AI market size would grow from a modest $150.2 billion USD in 2023 to a whopping $1,345.2 billion USD in 2030.

Growth creates demand. A McKinsey Report on the State of AI in 2022—and a Half Decade in Review noted that employers were eager for folks with AI skills. In the report’s timeframe, software engineers were hired most often—”another clear sign that many organizations have largely shifted from experimenting with AI to actively embedding it in enterprise applications.” Yet data scientists trained in AI remained scarce.

McKinsey’s report contained more intriguing facts about employer habits. Nearly half of respondents said that—when it came to sourcing AI talent—they were choosing to reskill existing employees or recruiting from technology companies. So you could argue that finding an AI-related job is more important than earning a master’s degree.

But these same employers also noted that they liked to recruit AI students from top-tier universities. And it’s an even bet that they would also consider graduates who had solid AI portfolios and a track record of engagement with industry partners through a university’s research lab. The trick is preparation.

How AI Is Used in Various Industries

Still unsure about your direction? Read up on AI developments across a broad swath of industries. You may find career opportunities in a realm that you never before considered.

• AI in Criminal Justice
• AI in Education
• AI in Finance
• AI in Healthcare
• AI in Manufacturing