High School

Deepfake professionals are computer experts, so be sure to take as many computer-related classes as possible in high school. Recommended courses include programming, software development, web design, computer science, computer security, and data analytics/science. Mathematics courses will also be useful. Take algebra, trigonometry, calculus, linear algebra, discrete mathematics, statistics, and applied mathematics courses. Other useful classes include English, speech, foreign language, business, psychology, and social studies.

Postsecondary Education

There are no degree programs in deepfake technology. As a result, deepfake professionals have a variety of educational backgrounds, but most have a minimum of a bachelor’s degree in software design or engineering, computer programming, computer security, data science, computer science, applied mathematics, or a related field. Many students combine study in one of the aforementioned fields with a certificate, minor, or specialization in AI, ML, or machine intelligence.

A few colleges and universities—such as Carnegie Mellon University, Indiana University, and Northeastern University—offer degree programs in artificial intelligence or machine learning.

Certification

A growing number of colleges and universities offer certificate programs in machine learning, artificial intelligence, and related fields. For example, Cornell University offers a certificate in machine learning to students who complete the following courses: Problem-Solving with Machine Learning; Estimating Probability Distributions; Learning with Linear Classifier; Decision Trees and Model Selection; Debugging and Improving Machine Learning Models; Learning with Kernel Machines; and Deep Learning and Neural Networks. Contact schools in your area to learn about available programs.

Other Education or Training

This is a cutting-edge field in which better ways to both create and detect deepfakes are constantly being developed. As a result, you’ll need to take continuing education classes throughout your career. These learning opportunities are offered by professional organizations such as the Association for Association for the Advancement of Artificial Intelligence (which offers a Symposium on Educational Advances in Artificial Intelligence at its Conference on Artificial Intelligence) and IEEE Computational Intelligence Society. You can also take courses at colleges and universities. For example, the Massachusetts Institute of Technology offers courses such as AI-Generated Media (Deepfakes for Good) and Media Literacy in the Age of Deepfakes. Other educational options are provided by cybersecurity education companies (such as INFOSEC) and online learning platforms (Coursera, edX, Udemy, Khan Academy, etc.).

Certification or Licensing

The Artificial Intelligence Board of America offers the artificial intelligence engineer credential to applicants who meet educational and work experience requirements and pass an examination. Visit https://www.artiba.org/certification/artificial-intelligence-certification to learn more.

Certification programs are also provided by software development firms. For example, SAS offers the AI and machine learning professional credential to applicants who complete five courses and pass several examinations.

Several years of experience working with deepfake technology is required for top positions, although some employers hire recent college graduates who have completed an internship in deepfake technology for entry-level positions.

Many deepfake professionals have backgrounds in machine learning engineering. Although skill requirements vary by employer and job title, here are some in-demand technical skills for machine learning engineers (MLEs) according to the recruiting firm Robert Half Technology and other sources:

• Knowledge of programming and software design fundamentals
• Knowledge of basic algorithms and object-oriented and functional design principles
• Extensive data modeling and data architecture skills
• Programming experience in Python, R, and Java (the top languages for MLEs), as well as in C++, C, JavaScript, Scala, and Julia
• Background in machine learning frameworks such as TensorFlow or Keras
• Knowledge of Hadoop or other distributed computing systems
• Experience working in an Agile environment
• Advanced math skills (linear algebra, Bayesian statistics, group theory)
• Experience with research and development protocols
• Experience in probability and statistics modeling procedures
• The ability to perform graphics processing unit programming

Other important traits for deepfake professionals include a detail-oriented personality, patience and the ability to concentrate for long periods of time, tenacity, creativity, strong communication and interpersonal skills, top-notch analytical and time-management ability, superior problem-solving acumen, and a willingness to continue to learn throughout their careers.