Artificial intelligence will transform the global economy, and AI jobs are in high demand.
Getting an education in AI is challenging and requires persistence and personal initiative.
AI careers are future-proof, meaning they are likely to survive well into the future.
Artificial intelligence (AI) promises to deliver some of the most significant and disruptive innovations of this century. Self-driving cars, robotic assistants, and automated disease diagnosis are all products of an emerging AI revolution that will reshape how we live and work. And with demand for talented engineers more than doubling in the last few years, there are limitless opportunities for professionals who want to work on the cutting edge of AI research and development.
While jobs designing and improving AI applications are growing, some analysts predict these efforts will disrupt economic activity in a big way. This is because AI systems can process unfathomable amounts of data, and human beings — meaning potentially millions of people in today's job market — simply aren't up to the task.
One recent report by the McKinsey Global Institute suggests that around one-third of the American workforce will need to switch occupations by 2030. Workers in data-heavy industries are especially vulnerable, including financial and administrative professionals, legal support staff, sales clerks, and IT workers.
More cautious industry observers point out that it's unclear what jobs may be lost and how many more will be created. The World Economic Forum, for example, predicts AI may lead to a net increase of 58 million jobs globally.
No matter what the new AI economy means for the future workforce, college students and young professionals stand to benefit from entering this burgeoning field. But breaking into artificial intelligence isn't as simple as learning computer science or earning a college degree. It takes initiative, guts, and know-how to engineer a career in AI. In fact, more than half of senior AI professionals report there is a skills gap in the recruitment of new scientists — a veritable "talent crisis," according to an Ernst & Young report.
Getting an AI Education: Intelligence Required
AI has a high learning curve, but for motivated students, the rewards of an AI career far outweigh the investment of time and energy. Succeeding in the field usually requires a bachelor's degree in computer science or a related discipline such as mathematics. More senior positions may require a master's or Ph.D., though a college degree is no longer considered a hard requirement by top employers like Apple and Google. Regardless, your success will depend largely on factors outside a formal education.
"Curiosity, confidence, and perseverance are good traits for any student looking to break into an emerging field, and AI is no exception," said Dan Ayoub, general manager for mixed reality education at Microsoft. Ayoub is a seasoned AI professional and agreed to talk to BestColleges about how to land a job in the field. "Unlike careers where a path has been laid over decades, AI is still in its infancy, which means you may have to form your own path and get creative."
What Ayoub means is that there is no standard artificial intelligence degree or curriculum. Some universities may not offer a prescribed set of courses for a major or specialization in AI, while those with dedicated AI programs may have unique approaches to the discipline.
"Because AI is still an emerging field ... universities differ in how specialized a degree you may be able to get," Ayoub said. "A good place to start is computer science coursework, getting familiar with the basics of data science, machine learning, and Java. … There are a number of new undergraduate and graduate programs popping up every day that are designed to prepare someone specifically to work in AI."
Artificial Intelligence Courses and Curriculum
As the table below demonstrates, AI consists of several overlapping disciplines. Understanding statistical methods, for example, is just as important as a background in computer science. In addition to the subjects listed here, it can be helpful to take interdisciplinary courses in areas like cognitive science to provide a conceptual framework for AI applications.
SAMPLE CORE SUBJECTS IN AN AI CURRICULUM
Math and Statistics
Linear Algebra
Differential and Integral Calculus
Matrices and Linear Transformations
Integration and Approximation
Modern Regression
Probability Theory
Bayesian Networking
Probabilistic Graphical Models
Computer Science
Computer Systems and Programming
Principles of Imperative Computation
Principles of Functional Programming
Data Science Essentials
Parallel and Sequential Data Structures and Algorithms
Logic Programming and Computational Logic
Agile Software Development
AI Core Subjects
Machine Learning, Deep Learning, and Reinforcement Learning
Information Theory, Inference, and Learning Algorithms
Neural Networks for Machine Learning
AI Representation and Problem-Solving
Natural Language Processing
Computer Vision and Image Analysis
Once you master some of the fundamentals, find the AI subfields that most interest you and shape your coursework accordingly. The next table shows more specialized subjects you might take as electives while earning a degree; these topics are also worth exploring at any stage of your career. Additional classes may be available that teach students specific AI applications in fields like biology, healthcare, and neuroscience.
SAMPLE ARTIFICIAL INTELLIGENCE CLUSTERS AND SUBJECTS
Machine Learning
Deep Reinforcement Learning and Control
Applied Machine Learning
Machine Learning for Text Mining
Advanced Data Analysis
Decision-Making and Robotics
Neural Computation
Autonomous Agents
Cognitive Robotics
Strategic Reasoning for AI
Robot Kinematics and Dynamics
Perception and Language
Information Retrieval and Search Engines
Speech Processing
Computational Perception
Computational Photography
Vision Sensors
Human-AI Interaction
Designing Human -Centered Systems
Human-Robot Interaction
Robotic Manipulation
Safe and Interactive Robots
Whether you're a college student or already in the workforce, it's important to proactively define your own AI curriculum. As Ayoub explained, "schools like Carnegie Mellon, Stanford, and MIT are just a few that have built out tracks for those wanting to work in AI, but there are many others. There are also supplemental programs that can help someone who is midcareer retrain to move into a job in AI."
For example, Microsoft recently announced an AI track for the Microsoft Professional Program, which is part of a larger effort that includes the developer-focused AI School. The programs are available online to anyone and, according to Ayoub, provide "job-ready skills and real-world experience to engineers and others who are looking to improve their skills in AI and data science through a series of online courses that feature hands-on labs and expert instructors."
Machine learning courses and related resources are also available from Google, MIT, Stanford, CalTech, and the University of Cambridge. With soaring demand, universities and big-name companies are eager to provide educational content that can help increase the supply of AI engineers.
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