What Percentage of Emerging Tech Jobs Focus on A.I.?

Some 29 percent of “emerging tech” jobs involve artificial intelligence (A.I.) skills, according to the latest CompTIA Tech Job Report.

Last month, CompTIA classified 65,241 jobs as “emerging tech,” and 19,100 of those involved artificial intelligence in some way (either as the job itself, or heavily relying on artificial intelligence skills). The top states for A.I. job postings included (in descending order) California, Texas, Virginia, New York, and Washington, DC—no surprise, as these tech hubs host a plethora of tech giants with the funding and talent necessary to build out solid A.I. stacks.

If you’re interested in mastering A.I. skills as part of your next job hunt, keep in mind that it all comes down to more than just mastering, say, TensorFlow or other tools. You’ll need to master a handful of more abstract, transferrable skills that’ll allow you to operate effectively in this brave new world.

Those skills (as we’ve broken down before) include:

• Critical thinking and problem-solving
• Data literacy
• Intellectual curiosity
• Commitment to continuous learning
• Identifying and addressing ethical challenges in tech/A.I.

If you want to quickly boost your knowledge about A.I. itself, multiple online resources can help you. For example, online learning portals such as Coursera have courses from the likes of DeepLearning.AI, IBM, Vanderbilt University, and other institutions. For those who want a certificate to slap on a resume, Udacity also offers a generative A.I. “nanodegree” program, with a focus on skills such as the OpenAI API, image pre-processing, and more; prerequisite skills include Python, neural networks, and more.

If you want to learn generative A.I. skills as fast as possible, Google also has online tutorials focusing on that tech, which include videos, quizzes, and short lessons:

Once you have those skills in place, A.I.-centric jobs will often ask you to use them to build models and help companies analyze data. For example, an average day for a machine learning engineer might involve the following:

• Researching, designing and implementing ML models and systems
• Implementing machine learning algorithms and tools
• Scaling data science prototypes
• Selecting appropriate data sets, verifying data quality, cleaning and organizing data (in collaboration with data engineers)
• Performing statistical analysis
• Executing tests and optimizing machine learning models and algorithms
• Monitoring systems in production and retraining them to improve performance
• Utilizing machine learning libraries

Yes, these skills are complicated and can take some time to learn—but demand for A.I.-related jobs is clearly high, and those who’ve mastered the necessary tools and tricks can earn premium pay. Last year, a study by Amazon Web Services (AWS) and Access Partnership found that employers “are willing to pay an average of 47 percent more for IT workers with A.I. skills.”